R Internals

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1 R Internal Structures

This chapter is the beginnings of documentation about R internal
structures. It is written for the core team and others studying the
code in the src/main directory.

It is a work-in-progress and should be checked against the current
version of the source code. Versions for R 2.x.y contain historical
comments about when features were introduced: this version is for the
3.x.y series.

1.1 SEXPs

What R users think of as variables or objects are
symbols which are bound to a value. The value can be thought of as
either a SEXP (a pointer), or the structure it points to, a
SEXPREC (and there are alternative forms used for vectors, namely
VECSXP pointing to VECTOR_SEXPREC structures).
So the basic building blocks of R objects are often called
nodes, meaning SEXPRECs or VECTOR_SEXPRECs.

Note that the internal structure of the SEXPREC is not made
available to R Extensions: rather SEXP is an opaque pointer,
and the internals can only be accessed by the functions provided.

Both types of node structure have as their first three fields a 32-bit
sxpinfo header and then three pointers (to the attributes and the
previous and next node in a doubly-linked list), and then some further
fields. On a 32-bit platform a node1 occupies 28 bytes: on a 64-bit platform typically 56
bytes (depending on alignment constraints).

The first five bits of the sxpinfo header specify one of up to 32
SEXPTYPEs.

1.1.1 SEXPTYPEs

Currently SEXPTYPEs 0:10 and 13:25 are in use. Values 11 and 12
were used for internal factors and ordered factors and have since been
withdrawn. Note that the SEXPTYPE numbers are stored in
saved objects and that the ordering of the types is used, so the
gap cannot easily be reused.

no

SEXPTYPE

Description

0

NILSXP

NULL

1

SYMSXP

symbols

2

LISTSXP

pairlists

3

CLOSXP

closures

4

ENVSXP

environments

5

PROMSXP

promises

6

LANGSXP

language objects

7

SPECIALSXP

special functions

8

BUILTINSXP

builtin functions

9

CHARSXP

internal character strings

10

LGLSXP

logical vectors

13

INTSXP

integer vectors

14

REALSXP

numeric vectors

15

CPLXSXP

complex vectors

16

STRSXP

character vectors

17

DOTSXP

dot-dot-dot object

18

ANYSXP

make “any” args work

19

VECSXP

list (generic vector)

20

EXPRSXP

expression vector

21

BCODESXP

byte code

22

EXTPTRSXP

external pointer

23

WEAKREFSXP

weak reference

24

RAWSXP

raw vector

25

S4SXP

S4 classes not of simple type

Many of these will be familiar from R level: the atomic vector types
are LGLSXP, INTSXP, REALSXP, CPLXSP,
STRSXP and RAWSXP. Lists are VECSXP and names
(also known as symbols) are SYMSXP. Pairlists (LISTSXP,
the name going back to the origins of R as a Scheme-like language)
are rarely seen at R level, but are for example used for argument
lists. Character vectors are effectively lists all of whose elements
are CHARSXP, a type that is rarely visible at R level.

Language objects (LANGSXP) are calls (including formulae and so
on). Internally they are pairlists with first element a
reference2 to the function to be called with remaining elements the
actual arguments for the call (and with the tags if present giving the
specified argument names). Although this is not enforced, many places
in the code assume that the pairlist is of length one or more, often
without checking.

Expressions are of type EXPRSXP: they are a vector of (usually
language) objects most often seen as the result of parse().

The functions are of types CLOSXP, SPECIALSXP and
BUILTINSXP: where SEXPTYPEs are stored in an integer
these are sometimes lumped into a pseudo-type FUNSXP with code
99. Functions defined via function are of type CLOSXP and
have formals, body and environment.

The SEXPTYPES4SXP is for S4 objects which do not consist
solely of a simple type such as an atomic vector or function.

The debug bit is used for closures and environments. For
closures it is set by debug() and unset by undebug(), and
indicates that evaluations of the function should be run under the
browser. For environments it indicates whether the browsing is in
single-step mode.

The trace bit is used for functions for trace() and for
other objects when tracing duplications (see tracemem).

The spare bit is used for closures to mark them for one
time debugging.

The named field is set and accessed by the SET_NAMED and
NAMED macros, and take values 0, 1 and 2.
R has a ‘call by value’ illusion, so an assignment like

b <- a

appears to make a copy of a and refer to it as b.
However, if neither a nor b are subsequently altered there
is no need to copy. What really happens is that a new symbol b
is bound to the same value as a and the named field on the
value object is set (in this case to 2). When an object is about
to be altered, the named field is consulted. A value of 2
means that the object must be duplicated before being changed. (Note
that this does not say that it is necessary to duplicate, only that it
should be duplicated whether necessary or not.) A value of 0
means that it is known that no other SEXP shares data with this
object, and so it may safely be altered. A value of 1 is used
for situations like

dim(a) <- c(7, 2)

where in principle two copies of a exist for the duration of the
computation as (in principle)

a <- `dim<-`(a, c(7, 2))

but for no longer, and so some primitive functions can be optimized to
avoid a copy in this case.

The gp bits are by definition ‘general purpose’. We label these
from 0 to 15. Bits 0–5 and bits 14–15 have been used as described below
(mainly from detective work on the sources).

The bits can be accessed and set by the LEVELS and
SETLEVELS macros, which names appear to date back to the internal
factor and ordered types and are now used in only a few places in the
code. The gp field is serialized/unserialized for the
SEXPTYPEs other than NILSXP, SYMSXP and
ENVSXP.

Bits 14 and 15 of gp are used for ‘fancy bindings’. Bit 14 is
used to lock a binding or an environment, and bit 15 is used to indicate
an active binding. (For the definition of an ‘active binding’ see the
header comments in file src/main/envir.c.) Bit 15 is used for an
environment to indicate if it participates in the global cache.

The macros ARGUSED and SET_ARGUSED are used when matching
actual and formal function arguments, and take the values 0, 1 and 2.

The macros MISSING and SET_MISSING are used for pairlists
of arguments. Four bits are reserved, but only two are used (and
exactly what for is not explained). It seems that bit 0 is used by
matchArgs to mark missingness on the returned argument list, and
bit 1 is used to mark the use of a default value for an argument copied
to the evaluation frame of a closure.

Bit 0 is used by macros DDVAL and SET_DDVAL. This
indicates that a SYMSXP is one of the symbols ..n which
are implicitly created when ... is processed, and so indicates
that it may need to be looked up in a DOTSXP.

Bit 0 is used for PRSEEN, a flag to indicate if a promise has
already been seen during the evaluation of the promise (and so to avoid
recursive loops).

Bit 0 is used for HASHASH, on the PRINTNAME of the
TAG of the frame of an environment. (This bit is not serialized
for CHARSXP objects.)

Bits 0 and 1 are used for weak references (to indicate ‘ready to
finalize’, ‘finalize on exit’).

Bit 0 is used by the condition handling system (on a VECSXP) to
indicate a calling handler.

Bit 4 is turned on to mark S4 objects.

Bits 1, 2, 3, 5 and 6 are used for a CHARSXP to denote its
encoding. Bit 1 indicates that the CHARSXP should be treated as
a set of bytes, not necessarily representing a character in any known
encoding. Bits 2, 3 and 6 are used to indicate that it is known to be
in Latin-1, UTF-8 or ASCII respectively.

Bit 5 for a CHARSXP indicates that it is hashed by its address,
that is NA_STRING or is in the CHARSXP cache (this is not
serialized). Only exceptionally is a CHARSXP not hashed, and
this should never happen in end-user code.

All of these alternatives apart from the first (an int) are three
pointers, so the union occupies three words.

The vector types are RAWSXP, CHARSXP, LGLSXP,
INTSXP, REALSXP, CPLXSXP, STRSXP,
VECSXP, EXPRSXP and WEAKREFSXP. Remember that such
types are a VECTOR_SEXPREC, which again consists of the header
and the same three pointers, but followed by two integers giving the
length and ‘true length’3 of the vector, and then followed by the data (aligned as
required: on most 32-bit systems with a 24-byte VECTOR_SEXPREC
node the data can follow immediately after the node). The data are a
block of memory of the appropriate length to store ‘true length’
elements (rounded up to a multiple of 8 bytes, with the 8-byte blocks
being the ‘Vcells’ referred in the documentation for gc()).

The ‘data’ for the various types are given in the table below. A lot of
this is interpretation, i.e. the types are not checked.

NILSXP

There is only one object of type NILSXP, R_NilValue, with
no data.

SYMSXP

Pointers to three nodes, the name, value and internal, accessed by
PRINTNAME (a CHARSXP), SYMVALUE and
INTERNAL. (If the symbol’s value is a .Internal function,
the last is a pointer to the appropriate SEXPREC.) Many symbols
have SYMVALUER_UnboundValue.

LISTSXP

Pointers to the CAR, CDR (usually a LISTSXP or NULL) and
TAG (a SYMSXP or NULL).

CLOSXP

Pointers to the formals (a pairlist), the body and the environment.

ENVSXP

Pointers to the frame, enclosing environment and hash table (NULL or a
VECSXP). A frame is a tagged pairlist with tag the symbol and
CAR the bound value.

PROMSXP

Pointers to the value, expression and environment (in which to evaluate
the expression). Once an promise has been evaluated, the environment is
set to NULL.

LANGSXP

A special type of LISTSXP used for function calls. (The CAR
references the function (perhaps via a symbol or language object), and
the CDR the argument list with tags for named arguments.) R-level
documentation references to ‘expressions’ / ‘language objects’ are
mainly LANGSXPs, but can be symbols (SYMSXPs) or
expression vectors (EXPRSXPs).

SPECIALSXP

BUILTINSXP

An integer giving the offset into the table of
primitives/.Internals.

CHARSXP

length, truelength followed by a block of bytes (allowing
for the nul terminator).

LGLSXP

INTSXP

length, truelength followed by a block of C ints
(which are 32 bits on all R platforms).

REALSXP

length, truelength followed by a block of C doubles.

CPLXSXP

length, truelength followed by a block of C99 double
complexs.

STRSXP

length, truelength followed by a block of pointers
(SEXPs pointing to CHARSXPs).

DOTSXP

A special type of LISTSXP for the value bound to a ...
symbol: a pairlist of promises.

ANYSXP

This is used as a place holder for any type: there are no actual objects
of this type.

VECSXP

EXPRSXP

length, truelength followed by a block of pointers. These
are internally identical (and identical to STRSXP) but differ in
the interpretations placed on the elements.

BCODESXP

For the ‘byte-code’ objects generated by the compiler.

EXTPTRSXP

Has three pointers, to the pointer, the protection value (an R object
which if alive protects this object) and a tag (a SYMSXP?).

WEAKREFSXP

A WEAKREFSXP is a special VECSXP of length 4, with
elements ‘key’, ‘value’, ‘finalizer’ and ‘next’.
The ‘key’ is NULL, an environment or an external pointer,
and the ‘finalizer’ is a function or NULL.

1.1.4 Allocation classes

As we have seen, the field gccls in the header is three bits to
label up to 8 classes of nodes. Non-vector nodes are of class 0, and
‘small’ vector nodes are of classes 1 to 5, with a class for custom
allocator vector nodes 6 and ‘large’ vector nodes being of class 7. The
‘small’ vector nodes are able to store vector data of up to 8, 16, 32,
64 and 128 bytes: larger vectors are malloc-ed individually
whereas the ‘small’ nodes are allocated from pages of about 2000
bytes. Vector nodes allocated using custom allocators (via
allocVector3) are not counted in the gc memory usage statistics
since their memory semantics is not under R’s control and may be
non-standard (e.g., memory could be partially shared across nodes).

1.2 Environments and variable lookup

What users think of as ‘variables’ are symbols which are bound to
objects in ‘environments’. The word ‘environment’ is used ambiguously
in R to mean either the frame of an ENVSXP (a pairlist
of symbol-value pairs) or an ENVSXP, a frame plus an
enclosure.

There are additional places that ‘variables’ can be looked up, called
‘user databases’ in comments in the code. These seem undocumented in
the R sources, but apparently refer to the RObjectTable package
at http://www.omegahat.org/RObjectTables/.

The base environment is special. There is an ENVSXP environment
with enclosure the empty environment R_EmptyEnv, but the frame of
that environment is not used. Rather its bindings are part of the
global symbol table, being those symbols in the global symbol table
whose values are not R_UnboundValue. When R is started the
internal functions are installed (by C code) in the symbol table, with
primitive functions having values and .Internal functions having
what would be their values in the field accessed by the INTERNAL
macro. Then .Platform and .Machine are computed and the
base package is loaded into the base environment followed by the system
profile.

The frames of environments (and the symbol table) are normally hashed
for faster access (including insertion and deletion).

By default R maintains a (hashed) global cache of ‘variables’ (that
is symbols and their bindings) which have been found, and this refers
only to environments which have been marked to participate, which
consists of the global environment (aka the user workspace), the base
environment plus environments4 which have been attached. When an environment is either
attached or detached, the names of its symbols are flushed
from the cache. The cache is used whenever searching for variables from
the global environment (possibly as part of a recursive search).

1.2.1 Search paths

S has the notion of a ‘search path’: the lookup for a ‘variable’
leads (possibly through a series of frames) to the ‘session frame’ the
‘working directory’ and then along the search path. The search path is
a series of databases (as returned by search()) which contain the
system functions (but not necessarily at the end of the path, as by
default the equivalent of packages are added at the end).

R has a variant on the S model. There is a search path (also
returned by search()) which consists of the global environment
(aka user workspace) followed by environments which have been attached
and finally the base environment. Note that unlike S it is not
possible to attach environments before the workspace nor after the base
environment.

However, the notion of variable lookup is more general in R, hence
the plural in the title of this subsection. Since environments have
enclosures, from any environment there is a search path found by looking
in the frame, then the frame of its enclosure and so on. Since loops
are not allowed, this process will eventually terminate: it can
terminate at either the base environment or the empty environment. (It
can be conceptually simpler to think of the search always terminating at
the empty environment, but with an optimization to stop at the base
environment.) So the ‘search path’ describes the chain of environments
which is traversed once the search reaches the global environment.

1.2.2 Namespaces

Namespaces are environments associated with packages (and once again
the base package is special and will be considered separately). A
package pkg with a namespace defines two environments
namespace:pkg and package:pkg: it is
package:pkg that can be attached and form part of
the search path.

The objects defined by the R code in the package are symbols with
bindings in the namespace:pkg environment. The
package:pkg environment is populated by selected symbols
from the namespace:pkg environment (the exports). The
enclosure of this environment is an environment populated with the
explicit imports from other namespaces, and the enclosure of
that environment is the base namespace. (So the illusion of the
imports being in the namespace environment is created via the
environment tree.) The enclosure of the base namespace is the global
environment, so the search from a package namespace goes via the
(explicit and implicit) imports to the standard ‘search path’.

The base namespace environment R_BaseNamespace is another
ENVSXP that is special-cased. It is effectively the same thing
as the base environment R_BaseEnvexcept that its
enclosure is the global environment rather than the empty environment:
the internal code diverts lookups in its frame to the global symbol
table.

1.2.3 Hash table

Environments in R usually have a hash table, and nowadays that is the
default in new.env(). It is stored as a VECSXP where
length is used for the allocated size of the table and
truelength is the number of primary slots in use—the pointer to
the VECSXP is part of the header of a SEXP of type
ENVSXP, and this points to R_NilValue if the environment
is not hashed.

For the pros and cons of hashing, see a basic text on Computer Science.

The code to implement hashed environments is in src/main/envir.c.
Unless set otherwise (e.g. by the size argument of
new.env()) the initial table size is 29. The table will
be resized by a factor of 1.2 once the load factor (the proportion of
primary slots in use) reaches 85%.

The hash chains are stored as pairlist elements of the VECSXP:
items are inserted at the front of the pairlist. Hashing is principally
designed for fast searching of environments, which are from time to time
added to but rarely deleted from, so items are not actually deleted but
have their value set to R_UnboundValue.

1.3 Attributes

As we have seen, every SEXPREC has a pointer to the attributes of
the node (default R_NilValue). The attributes can be
accessed/set by the macros/functions ATTRIB and
SET_ATTRIB, but such direct access is normally only used to check
if the attributes are NULL or to reset them. Otherwise access
goes through the functions getAttrib and setAttrib which
impose restrictions on the attributes. One thing to watch is that if
you copy attributes from one object to another you may (un)set the
"class" attribute and so need to copy the object and S4 bits as
well. There is a macro/function DUPLICATE_ATTRIB to automate
this.

Note that the ‘attributes’ of a CHARSXP are used as part of the
management of the CHARSXP cache: of course CHARSXP’s are
not user-visible but C-level code might look at their attributes.

The code assumes that the attributes of a node are either
R_NilValue or a pairlist of non-zero length (and this is checked
by SET_ATTRIB). The attributes are named (via tags on the
pairlist). The replacement function attributes<- ensures that
"dim" precedes "dimnames" in the pairlist. Attribute
"dim" is one of several that is treated specially: the values are
checked, and any "names" and "dimnames" attributes are
removed. Similarly, you cannot set "dimnames" without having set
"dim", and the value assigned must be a list of the correct
length and with elements of the correct lengths (and all zero-length
elements are replaced by NULL).

The other attributes which are given special treatment are
"names", "class", "tsp", "comment" and
"row.names". For pairlist-like objects the names are not stored
as an attribute but (as symbols) as the tags: however the R interface
makes them look like conventional attributes, and for one-dimensional
arrays they are stored as the first element of the "dimnames"
attribute. The C code ensures that the "tsp" attribute is an
REALSXP, the frequency is positive and the implied length agrees
with the number of rows of the object being assigned to. Classes and
comments are restricted to character vectors, and assigning a
zero-length comment or class removes the attribute. Setting or removing
a "class" attribute sets the object bit appropriately. Integer
row names are converted to and from the internal compact representation.

Care needs to be taken when adding attributes to objects of the types
with non-standard copying semantics. There is only one object of type
NILSXP, R_NilValue, and that should never have attributes
(and this is enforced in installAttrib). For environments,
external pointers and weak references, the attributes should be relevant
to all uses of the object: it is for example reasonable to have a name
for an environment, and also a "path" attribute for those
environments populated from R code in a package.

When should attributes be preserved under operations on an object?
Becker, Chambers & Wilks (1988, pp. 144–6) give some guidance. Scalar
functions (those which operate element-by-element on a vector and whose
output is similar to the input) should preserve attributes (except
perhaps class, and if they do preserve class they need to preserve the
OBJECT and S4 bits). Binary operations normally call
copyMostAttributes to copy most attributes from the longer
argument (and if they are of the same length from both, preferring the
values on the first). Here ‘most’ means all except the names,
dim and dimnames which are set appropriately by the code
for the operator.

Subsetting (other than by an empty index) generally drops all attributes
except names, dim and dimnames which are reset as
appropriate. On the other hand, subassignment generally preserves such
attributes even if the length is changed. Coercion drops all
attributes. For example:

1.4 Contexts

Contexts are the internal mechanism used to keep track of where a
computation has got to (and from where), so that control-flow constructs
can work and reasonable information can be produced on error conditions
(such as via traceback), and otherwise (the sys.xxx
functions).

where the CTXT_FUNCTION bit is on wherever function closures are
involved.

Contexts are created by a call to begincontext and ended by a
call to endcontext: code can search up the stack for a
particular type of context via findcontext (and jump there) or
jump to a specific context via R_JumpToContext.
R_ToplevelContext is the ‘idle’ state (normally the command
prompt), and R_GlobalContext is the top of the stack.

Note that whilst calls to closures and builtins set a context, those to special
internal functions never do.

Dispatching from a S3 generic (via UseMethod or its internal
equivalent) or calling NextMethod sets the context type to
CTXT_GENERIC. This is used to set the sysparent of the
method call to that of the generic, so the method appears to have
been called in place of the generic rather than from the generic.

The R sys.frame and sys.call functions work by counting
calls to closures (type CTXT_FUNCTION) from either end of the
context stack.

Note that the sysparent element of the structure is not the same
thing as sys.parent(). Element sysparent is primarily
used in managing changes of the function being evaluated, i.e. by
Recall and method dispatch.

CTXT_CCODE contexts are currently used in cat(),
load(), scan() and write.table() (to close the
connection on error), by PROTECT, serialization (to recover from
errors, e.g. free buffers) and within the error handling code (to
raise the C stack limit and reset some variables).

1.5 Argument evaluation

As we have seen, functions in R come in three types, closures
(SEXPTYPECLOSXP), specials (SPECIALSXP) and
builtins (BUILTINSXP). In this section we consider when (and if)
the actual arguments of function calls are evaluated. The rules are
different for the internal (special/builtin) and R-level functions
(closures).

For a call to a closure, the actual and formal arguments are matched and
a matched call (another LANGSXP) is constructed. This process
first replaces the actual argument list by a list of promises to the
values supplied. It then constructs a new environment which contains
the names of the formal parameters matched to actual or default values:
all the matched values are promises, the defaults as promises to be
evaluated in the environment just created. That environment is then
used for the evaluation of the body of the function, and promises will
be forced (and hence actual or default arguments evaluated) when they
are encountered.
(Evaluating a promise sets NAMED = 2 on its value, so if the
argument was a symbol its binding is regarded as having multiple
references during the evaluation of the closure call.)

If the closure is an S3 generic (that is, contains a call to
UseMethod) the evaluation process is the same until the
UseMethod call is encountered. At that point the argument on
which to do dispatch (normally the first) will be evaluated if it has
not been already. If a method has been found which is a closure, a new
evaluation environment is created for it containing the matched
arguments of the method plus any new variables defined so far during the
evaluation of the body of the generic. (Note that this means changes to
the values of the formal arguments in the body of the generic are
discarded when calling the method, but actual argument promises
which have been forced retain the values found when they were forced.
On the other hand, missing arguments have values which are promises to
use the default supplied by the method and not by the generic.) If the
method found is a primitive it is called with the matched argument list
of promises (possibly already forced) used for the generic.

The essential difference5 between special and builtin functions is
that the arguments of specials are not evaluated before the C code is
called, and those of builtins are. Note that being a special/builtin is
separate from being primitive or .Internal: quote is a
special primitive, + is a builtin primitive, cbind is a
special .Internal and grep is a builtin .Internal.

Many of the internal functions are internal generics, which for specials
means that they do not evaluate their arguments on call, but the C code
starts with a call to DispatchOrEval. The latter evaluates the
first argument, and looks for a method based on its class. (If S4
dispatch is on, S4 methods are looked for first, even for S3 classes.)
If it finds a method, it dispatches to that method with a call based on
promises to evaluate the remaining arguments. If no method is found,
the remaining arguments are evaluated before return to the internal
generic.

The other way that internal functions can be generic is to be group
generic. Most such functions are builtins (so immediately evaluate all
their arguments), and all contain a call to the C function
DispatchGeneric. There are some peculiarities over the number of
arguments for the "Math" group generic, with some members
allowing only one argument, some having two (with a default for the
second) and trunc allows one or more but the default method only
accepts one.

1.5.1 Missingness

Actual arguments to (non-internal) R functions can be fewer than are
required to match the formal arguments of the function. Having
unmatched formal arguments will not matter if the argument is never used
(by lazy evaluation), but when the argument is evaluated, either its
default value is evaluated (within the evaluation environment of the
function) or an error is thrown with a message along the lines of

argument "foobar" is missing, with no default

Internally missingness is handled by two mechanisms. The object
R_MissingArg is used to indicate that a formal argument has no
(default) value. When matching the actual arguments to the formal
arguments, a new argument list is constructed from the formals all of
whose values are R_MissingArg with the first MISSING bit
set. Then whenever a formal argument is matched to an actual argument,
the corresponding member of the new argument list has its value set to
that of the matched actual argument, and if that is not
R_MissingArg the missing bit is unset.

This new argument list is used to form the evaluation frame for the
function, and if named arguments are subsequently given a new value
(before they are evaluated) the missing bit is cleared.

Missingness of arguments can be interrogated via the missing()
function. An argument is clearly missing if its missing bit is set or
if the value is R_MissingArg. However, missingness can be passed
on from function to function, for using a formal argument as an actual
argument in a function call does not count as evaluation. So
missing() has to examine the value (a promise) of a
non-yet-evaluated formal argument to see if it might be missing, which
might involve investigating a promise and so on ….

Special primitives also need to handle missing arguments, and in some
case (e.g. log) that is why they are special and not
builtin. This is usually done by testing if an argument’s value is
R_MissingArg.

1.5.2 Dot-dot-dot arguments

Dot-dot-dot arguments are convenient when writing functions, but
complicate the internal code for argument evaluation.

The formals of a function with a ... argument represent that as a
single argument like any other argument, with tag the symbol
R_DotsSymbol. When the actual arguments are matched to the
formals, the value of the ... argument is of SEXPTYPEDOTSXP, a pairlist of promises (as used for matched arguments)
but distinguished by the SEXPTYPE.

Recall that the evaluation frame for a function initially contains the
name=value pairs from the matched call, and hence
this will be true for ... as well. The value of ... is a
(special) pairlist whose elements are referred to by the special symbols
..1, ..2, … which have the DDVAL bit set:
when one of these is encountered it is looked up (via ddfindVar)
in the value of the ... symbol in the evaluation frame.

Values of arguments matched to a ... argument can be missing.

Special primitives may need to handle ... arguments: see for
example the internal code of switch in file
src/main/builtin.c.

1.6 Autoprinting

Whether the returned value of a top-level R expression is printed is
controlled by the global boolean variable R_Visible. This is set
(to true or false) on entry to all primitive and internal functions
based on the eval column of the table in file
src/main/names.c: the appropriate setting can be extracted by the
macro PRIMPRINT.

The R primitive function invisible makes use of this
mechanism: it just sets R_Visible = FALSE before entry and
returns its argument.

For most functions the intention will be that the setting of
R_Visible when they are entered is the setting used when they
return, but there need to be exceptions. The R functions
identify, options, system and writeBin
determine whether the result should be visible from the arguments or
user action. Other functions themselves dispatch functions which may
change the visibility flag: examples6 are
.Internal, do.call, eval, withVisible,
if, NextMethod, Recall, recordGraphics,
standardGeneric, switch and UseMethod.

‘Special’ primitive and internal functions evaluate their arguments
internally afterR_Visible has been set, and evaluation of
the arguments (e.g. an assignment as in PR#9263)) can change the value
of the flag.

The R_Visible flag can also get altered during the evaluation of
a function, with comments in the code about warning,
writeChar and graphics functions calling GText (PR#7397).
(Since the C-level function eval sets R_Visible, this
could apply to any function calling it. Since it is called when
evaluating promises, even object lookup can change R_Visible.)
Internal and primitive functions force the documented setting of
R_Visible on return, unless the C code is allowed to change it
(the exceptions above are indicated by PRIMPRINT having value 2).

The actual autoprinting is done by PrintValueEnv in file
print.c. If the object to be printed has the S4 bit set and S4
methods dispatch is on, show is called to print the object.
Otherwise, if the object bit is set (so the object has a
"class" attribute), print is called to dispatch methods:
for objects without a class the internal code of print.default
is called.

1.7 The write barrier and the garbage collector

R has long had a generational garbage collector, and bit gcgen
in the sxpinfo header is used in the implementation of this.
This is used in conjunction with the mark bit to identify two
previous generations.

There are three levels of collections. Level 0 collects only the
youngest generation, level 1 collects the two youngest generations and
level 2 collects all generations. After 20 level-0 collections the next
collection is at level 1, and after 5 level-1 collections at level 2.
Further, if a level-n collection fails to provide 20% free space
(for each of nodes and the vector heap), the next collection will be at
level n+1. (The R-level function gc() performs a
level-2 collection.)

A generational collector needs to efficiently ‘age’ the objects,
especially list-like objects (including STRSXPs). This is done
by ensuring that the elements of a list are regarded as at least as old
as the list when they are assigned. This is handled by the
functions SET_VECTOR_ELT and SET_STRING_ELT, which is why
they are functions and not macros. Ensuring the integrity of such
operations is termed the write barrier and is done by making the
SEXP opaque and only providing access via functions (which cannot
be used as lvalues in assignments in C).

All code in R extensions is by default behind the write barrier. The
only way to obtain direct access to the internals of the SEXPRECs
is to define ‘USE_RINTERNALS’ before including header file
Rinternals.h, which is normally defined in Defn.h. To
enable a check on the way that the access is used, R can be compiled
with flag --enable-strict-barrier which ensures that header
Defn.h does not define ‘USE_RINTERNALS’ and hence that
SEXP is opaque in most of R itself. (There are some necessary
exceptions: foremost in file memory.c where the accessor
functions are defined and also in file size.c which needs access
to the sizes of the internal structures.)

1.8 Serialization Formats

Serialized versions of R objects are used by load/save
and also at a slightly lower level by saveRDS/readRDS (and
their earlier ‘internal’ dot-name versions) and
serialize/unserialize. These differ in what they
serialize to (a file, a connection, a raw vector) and whether they are
intended to serialize a single object or a collection of objects
(typically the workspace). save writes a header at the beginning
of the file (a single LF-terminated line) which the lower-level versions
do not.

save and saveRDS allow various forms of compression, and
gzip compression is the default (except for ASCII
saves). Compression is applied to the whole file stream, including the
headers, so serialized files can be uncompressed or re-compressed by
external programs. Both load and readRDS can read
gzip, bzip2 and xz forms of compression
when reading from a file, and gzip compression when reading
from a connection.

R has used the same serialization format since R 1.4.0 in December
2001. Earlier formats are still supported via load and
save but such formats are not described here. The current
serialization format is called ‘version 2’, and has been expanded in
back-compatible ways since its inception, for example to support
additional SEXPTYPEs.

save works by writing a single-line header (typically
RDX2\n for a binary save: the only other current value is
RDA2\n for save(files=TRUE)), then creating a tagged
pairlist of the objects to be saved and serializing that single object.
load reads the header line, unserializes a single object (a
pairlist or a vector list) and assigns the elements of the object in the
specified environment. The header line serves two purposes in R: it
identifies the serialization format so load can switch to the
appropriate reader code, and the linefeed allows the detection of files
which have been subjected to a non-binary transfer which re-mapped line
endings. It can also be thought of as a ‘magic number’ in the sense
used by the file program (although R save files are not yet
by default known to that program).

Serialization in R needs to take into account that objects may
contain references to environments, which then have enclosing
environments and so on. (Environments recognized as package or name
space environments are saved by name.) There are ‘reference objects’
which are not duplicated on copy and should remain shared on
unserialization. These are weak references, external pointers and
environments other than those associated with packages, namespaces and
the global environment. These are handled via a hash table, and
references after the first are written out as a reference marker indexed
by the table entry.

Version-2 serialization first writes a header indicating the format
(normally ‘X\n’ for an XDR format binary save, but ‘A\n’,
ASCII, and ‘B\n’, native word-order binary, can also occur) and
then three integers giving the version of the format and two R
versions (packed by the R_Version macro from Rversion.h).
(Unserialization interprets the two versions as the version of R
which wrote the file followed by the minimal version of R needed to
read the format.) Serialization then writes out the object recursively
using function WriteItem in file src/main/serialize.c.

Some objects are written as if they were SEXPTYPEs: such
pseudo-SEXPTYPEs cover R_NilValue, R_EmptyEnv,
R_BaseEnv, R_GlobalEnv, R_UnboundValue,
R_MissingArg and R_BaseNamespace.

For all SEXPTYPEs except NILSXP, SYMSXP and
ENVSXP serialization starts with an integer with the
SEXPTYPE in bits 0:77 followed by the object bit, two bits
indicating if there are any attributes and if there is a tag (for the
pairlist types), an unused bit and then the gp
field8 in
bits 12:27. Pairlist-like objects write their attributes (if any), tag
(if any), CAR and then CDR (using tail recursion): other objects write
their attributes after themselves. Atomic vector objects write their
length followed by the data: generic vector-list objects write their
length followed by a call to WriteItem for each element. The
code for CHARSXPs special-cases NA_STRING and writes it as
length -1 with no data. Lengths no more than 2^31 - 1 are
written in that way and larger lengths (which only occur on 64-bit
systems) as -1 followed by the upper and lower 32-bits as integers
(regarded as unsigned).

Environments are treated in several ways: as we have seen, some are
written as specific pseudo-SEXPTYPEs. Package and namespace
environments are written with pseudo-SEXPTYPEs followed by the
name. ‘Normal’ environments are written out as ENVSXPs with an
integer indicating if the environment is locked followed by the
enclosure, frame, ‘tag’ (the hash table) and attributes.

In the ‘XDR’ format integers and doubles are written in bigendian order:
however the format is not fully XDR (as defined in RFC 1832) as byte
quantities (such as the contents of CHARSXP and RAWSXP
types) are written as-is and not padded to a multiple of four bytes.

The ‘ASCII’ format writes 7-bit characters. Integers are formatted with
%d (except that NA_integer_ is written as NA),
doubles formatted with %.16g (plus NA, Inf and
-Inf) and bytes with %02x. Strings are written using
standard escapes (e.g. \t and \013) for non-printing and
non-ASCII bytes.

1.9 Encodings for CHARSXPs

Character data in R are stored in the sexptype CHARSXP.

There is support for encodings other than that of the current locale, in
particular UTF-8 and the multi-byte encodings used on Windows for CJK
languages. A limited means to indicate the encoding of a CHARSXP
is via two of the ‘general purpose’ bits which are used to declare
the encoding to be either Latin-1 or UTF-8. (Note that it is possible
for a character vector to contain elements in different encodings.)
Both printing and plotting notice the declaration and convert the string
to the current locale (possibly using <xx> to display in
hexadecimal bytes that are not valid in the current locale). Many (but
not all) of the character manipulation functions will either preserve
the declaration or re-encode the character string.

Strings that refer to the OS such as file names need to be passed
through a wide-character interface on some OSes (e.g. Windows).

When are character strings declared to be of known encoding? One way is
to do so directly via Encoding. The parser declares the encoding
if this is known, either via the encoding argument to
parse or from the locale within which parsing is being done at
the R command line. (Other ways are recorded on the help page for
Encoding.)

It is not necessary to declare the encoding of ASCII strings
as they will work in any locale. ASCII strings should never
have a marked encoding, as any encoding will be ignored when entering
such strings into the CHARSXP cache.

The rationale behind considering only UTF-8 and Latin-1 was that most
systems are capable of producing UTF-8 strings and this is the nearest
we have to a universal format. For those that do not (for example those
lacking a powerful enough iconv), it is likely that they work in
Latin-1, the old R assumption. The the parser can return a
UTF-8-encoded string if it encounters a ‘\uxxx’ escape for a
Unicode point that cannot be represented in the current charset. (This
needs MBCS support, and was only enabled9 on
Windows.) This is enabled for all platforms, and a ‘\uxxx’ or
‘\Uxxxxxxxx’ escape ensures that the parsed string will be marked
as UTF-8.

Most of the character manipulation functions now preserve UTF-8
encodings: there are some notes as to which at the top of file
src/main/character.c and in file
src/library/base/man/Encoding.Rd.

Graphics devices are offered the possibility of handing UTF-8-encoded
strings without re-encoding to the native character set, by setting
hasTextUTF8 to be ‘TRUE’ and supplying functions
textUTF8 and strWidthUTF8 that expect UTF-8-encoded
inputs. Normally the symbol font is encoded in Adobe Symbol encoding,
but that can be re-encoded to UTF-8 by setting wantSymbolUTF8 to
‘TRUE’. The Windows’ port of cairographics has a rather peculiar
assumption: it wants the symbol font to be encoded in UTF-8 as if it
were encoded in Latin-1 rather than Adobe Symbol: this is selected by
wantSymbolUTF8 = NA_LOGICAL.

Windows has no UTF-8 locales, but rather expects to work with
UCS-210 strings.
R (being written in standard C) would not work internally with UCS-2
without extensive changes. The Rgui console11 uses UCS-2 internally, but communicates with the R
engine in the native encoding. To allow UTF-8 strings to be printed in
UTF-8 in Rgui.exe, an escape convention is used (see header file
rgui_UTF8.h) which is used by cat, print and
autoprinting.

‘Unicode’ (UCS-2LE) files are common in the Windows world, and
readLines and scan will read them into UTF-8 strings on
Windows if the encoding is declared explicitly on an unopened
connection passed to those functions.

1.10 The CHARSXP cache

There is a global cache for CHARSXPs created by mkChar —
the cache ensures that most CHARSXPs with the same contents share
storage (‘contents’ including any declared encoding). Not all
CHARSXPs are part of the cache – notably ‘NA_STRING’ is
not. CHARSXPs reloaded from the save formats of R prior
to 0.99.0 are not cached (since the code used is frozen and very few
examples still exist).

The cache records the encoding of the string as well as the bytes: all
requests to create a CHARSXP should be via a call to
mkCharLenCE. Any encoding given in mkCharLenCE call will
be ignored if the string’s bytes are all ASCII characters.

1.11 Warnings and errors

Each of warning and stop have two C-level equivalents,
warning, warningcall, error and errorcall.
The relationship between the pairs is similar: warning tries to
fathom out a suitable call, and then calls warningcall with that
call as the first argument if it succeeds, and with call =
R_NilValue if it does not. When warningcall is called, it
includes the deparsed call in its printout unless call =
R_NilValue.

warning and error look at the context stack. If the
topmost context is not of type CTXT_BUILTIN, it is used to
provide the call, otherwise the next context provides the call.
This means that when these functions are called from a primitive or
.Internal, the imputed call will not be to
primitive/.Internal but to the function calling the
primitive/.Internal . This is exactly what one wants for a
.Internal, as this will give the call to the closure wrapper.
(Further, for a .Internal, the call is the argument to
.Internal, and so may not correspond to any R function.)
However, it is unlikely to be what is needed for a primitive.

The upshot is that that warningcall and errorcall should
normally be used for code called from a primitive, and warning
and error should be used for code called from a .Internal
(and necessarily from .Call, .C and so on, where the call
is not passed down). However, there are two complications. One is that
code might be called from either a primitive or a .Internal, in
which case probably warningcall is more appropriate. The other
involves replacement functions, where the call was once of the form

which is unpalatable to the end user. For replacement functions there
will be a suitable context at the top of the stack, so warning
should be used. (The results for .Internal replacement functions
such as substr<- are not ideal.)

1.12.1 Representation of S4 objects

S4 objects can be of any SEXPTYPE. They are either an object of
a simple type (such as an atomic vector or function) with S4 class
information or of type S4SXP. In all cases, the ‘S4 bit’ (bit 4
of the ‘general purpose’ field) is set, and can be tested by the
macro/function IS_S4_OBJECT.

S4 objects are created via new()12 and thence via the C
function R_do_new_object. This duplicates the prototype of the
class, adds a class attribute and sets the S4 bit. All S4 class
attributes should be character vectors of length one with an attribute
giving (as a character string) the name of the package (or
.GlobalEnv) containing the class definition. Since S4 objects
have a class attribute, the OBJECT bit is set.

It is currently unclear what should happen if the class attribute is
removed from an S4 object, or if this should be allowed.

1.12.2 S4 classes

S4 classes are stored as R objects in the environment in which they
are created, with names .__C__classname: as such they are
not listed by default by ls.

The objects are S4 objects of class "classRepresentation" which
is defined in the methods package.

Since these are just objects, they are subject to the normal scoping
rules and can be imported and exported from namespaces like other
objects. The directives importClassesFrom and
exportClasses are merely convenient ways to refer to class
objects without needing to know their internal ‘metaname’ (although
exportClasses does a little sanity checking via isClass).

1.12.3 S4 methods

Details of methods are stored in S4 objects of class
"MethodsList". They have a non-syntactic name of the form
.__M__generic:package for all methods defined in the
current environment for the named generic derived from a specific
package (which might be .GlobalEnv).

There is also environment .__T__generic:package which
has names the signatures of the methods defined, and values the
corresponding method functions. This is often referred to as a ‘methods
table’.

When a package without a namespace is attached these objects become
visible on the search path. library calls
methods:::cacheMetaData to update the internal tables.

During an R session there is an environment associated with each
non-primitive generic containing objects .AllMTable,
.Generic, .Methods, .MTable, .SigArgs and
.SigLength. .MTable and AllMTable are merged
methods tables containing all the methods defined directly and via
inheritance respectively. .Methods is a merged methods list.

Exporting methods from a namespace is more complicated than exporting a
class. Note first that you do not export a method, but rather the
directive exportMethods will export all the methods defined in
the namespace for a specified generic: the code also adds to the list
of generics any that are exported directly. For generics which are
listed via exportMethods or exported themselves, the
corresponding "MethodsList" and environment are exported and so
will appear (as hidden objects) in the package environment.

Methods for primitives which are internally S4 generic (see below) are
always exported, whether mentioned in the NAMESPACE file or not.

Methods can be imported either via the directive
importMethodsFrom or via importing a namespace by import.
Also, if a generic is imported via importFrom, its methods are
also imported. In all cases the generic will be imported if it is in
the namespace, so importMethodsFrom is most appropriate for
methods defined on generics in other packages. Since methods for a
generic could be imported from several different packages, the methods
tables are merged.

When a package with a namespace is attached
methods:::cacheMetaData is called to update the internal tables:
only the visible methods will be cached.

1.12.4 Mechanics of S4 dispatch

For all but primitive functions, setting a method on an existing
function that is not itself S4 generic creates a new object in the
current environment which is a call to standardGeneric with the
old definition as the default method. Such S4 generics can also be
created via a call to setGeneric13 and are standard closures
in the R language, with environment the environment within which they
are created. With the advent of namespaces this is somewhat
problematic: if myfn was previously in a package with a name
space there will be two functions called myfn on the search
paths, and which will be called depends on which search path is in use.
This is starkest for functions in the base namespace, where the
original will be found ahead of the newly created function from any
other package with a namespace.

Primitive functions are treated quite differently, for efficiency
reasons: this results in different semantics. setGeneric is
disallowed for primitive functions. The methods namespace
contains a list .BasicFunsList named by primitive functions:
the entries are either FALSE or a standard S4 generic showing
the effective definition. When setMethod (or
setReplaceMethod) is called, it either fails (if the list entry
is FALSE) or a method is set on the effective generic given in
the list.

Actual dispatch of S4 methods for almost all primitives piggy-backs on
the S3 dispatch mechanism, so S4 methods can only be dispatched for
primitives which are internally S3 generic. When a primitive that is
internally S3 generic is called with a first argument which is an S4
object and S4 dispatch is on (that is, the methods namespace is
loaded), DispatchOrEval calls R_possible_dispatch (defined
in file src/main/objects.c). (Members of the S3 group generics,
which includes all the generic operators, are treated slightly
differently: the first two arguments are checked and
DispatchGroup is called.) R_possible_dispatch first
checks an internal table to see if any S4 methods are set for that
generic (and S4 dispatch is currently enabled for that generic), and if
so proceeds to S4 dispatch using methods stored in another internal
table. All primitives are in the base namespace, and this mechanism
means that S4 methods can be set for (some) primitives and will always
be used, in contrast to setting methods on non-primitives.

The exception is %*%, which is S4 generic but not S3 generic as
its C code contains a direct call to R_possible_dispatch.

The primitive as.double is special, as as.numeric and
as.real are copies of it. The methods package code partly
refers to generics by name and partly by function, and maps
as.double and as.real to as.numeric (since that is
the name used by packages exporting methods for it).

Some elements of the language are implemented as primitives, for example
}. This includes the subset and subassignment ‘functions’ and
they are S4 generic, again piggybacking on S3 dispatch.

.BasicFunsList is generated when methods is installed, by
computing all primitives, initially disallowing methods on all and then
setting generics for members of .GenericArgsEnv, the S4 group
generics and a short exceptions list in file BasicFunsList.R: this
currently contains the subsetting and subassignment operators and an
override for c.

1.13 Memory allocators

R’s memory allocation is almost all done via routines in file
src/main/memory.c. It is important to keep track of where memory
is allocated, as the Windows port (by default) makes use of a memory
allocator that differs from malloc etc as provided by MinGW.
Specifically, there are entry points Rm_malloc, Rm_free,
Rm_calloc and Rm_free provided by file
src/gnuwin32/malloc.c. This was done for two reasons. The
primary motivation was performance: the allocator provided by MSVCRT
via MinGW was far too slow at handling the many small allocations
that the allocation system for SEXPRECs uses. As a side benefit,
we can set a limit on the amount of allocated memory: this is useful as
whereas Windows does provide virtual memory it is relatively far slower
than many other R platforms and so limiting R’s use of swapping is
highly advantageous. The high-performance allocator is only called from
src/main/memory.c, src/main/regex.c, src/extra/pcre
and src/extra/xdr: note that this means that it is not used in
packages.

The rest of R should where possible make use of the allocators made
available by file src/main/memory.c, which are also the methods
recommended in
Memory allocation in Writing R Extensions
for use in R packages, namely the use of R_alloc,
Calloc, Realloc and Free. Memory allocated by
R_alloc is freed by the garbage collector once the ‘watermark’
has been reset by calling
vmaxset. This is done automatically by the wrapper code calling
primitives and .Internal functions (and also by the wrapper code
to .Call and .External), but
vmaxget and vmaxset can be used to reset the watermark
from within internal code if the memory is only required for a short
time.

All of the methods of memory allocation mentioned so far are relatively
expensive. All R platforms support alloca, and in almost all
cases14 this is managed by the
compiler, allocates memory on the C stack and is very efficient.

There are two disadvantages in using alloca. First, it is
fragile and care is needed to avoid writing (or even reading) outside
the bounds of the allocation block returned. Second, it increases the
danger of overflowing the C stack. It is suggested that it is only
used for smallish allocations (up to tens of thousands of bytes), and
that

R_CheckStack();

is called immediately after the allocation (as R’s stack checking
mechanism will warn far enough from the stack limit to allow for modest
use of alloca). (do_makeunique in file src/main/unique.c
provides an example of both points.)

There is an alternative check,

R_CheckStack2(size_t extra);

to be called immediately before trying an allocation of
extra bytes.

An alternative strategy has been used for various functions which
require intermediate blocks of storage of varying but usually small
size, and this has been consolidated into the routines in the header
file src/main/RBufferUtils.h. This uses a structure which
contains a buffer, the current size and the default size. A call to

R_AllocStringBuffer(size_t blen, R_StringBuffer *buf);

sets buf->data to a memory area of at least blen+1 bytes.
At least the default size is used, which means that for small
allocations the same buffer can be reused. A call to
R_FreeStringBufferL releases memory if more than the default has
been allocated whereas a call to R_FreeStringBuffer frees any
memory allocated.

The R_StringBuffer structure needs to be initialized, for example by

static R_StringBuffer ex_buff = {NULL, 0, MAXELTSIZE};

which uses a default size of MAXELTSIZE = 8192 bytes. Most
current uses have a static R_StringBuffer structure, which
allows the (default-sized) buffer to be shared between calls to e.g.
grep and even between functions: this will need to be changed if
R ever allows concurrent evaluation threads. So the idiom is

1.13.1 Internals of R_alloc

The memory used by R_alloc is allocated as R vectors, of type
RAWSXP. Thus the allocation is in units of 8 bytes, and is
rounded up. A request for zero bytes currently returns NULL (but
this should not be relied on). For historical reasons, in all other
cases 1 byte is added before rounding up so the allocation is always
1–8 bytes more than was asked for: again this should not be relied on.

The vectors allocated are protected via the setting of R_VStack,
as the garbage collector marks everything that can be reached from that
location. When a vector is R_allocated, its ATTRIB
pointer is set to the current R_VStack, and R_VStack is
set to the latest allocation. Thus R_VStack is a single-linked
chain of the vectors currently allocated via R_alloc. Function
vmaxset resets the location R_VStack, and should be to a
value that has previously be obtained viavmaxget:
allocations after the value was obtained will no longer be protected and
hence available for garbage collection.

1.14.1 Base environment

The graphics devices system maintains two variables .Device and
.Devices in the base environment: both are always set. The
variable .Devices gives a list of character vectors of the names
of open devices, and .Device is the element corresponding to the
currently active device. The null device will always be open.

There appears to be a variable .Options, a pairlist giving the
current options settings. But in fact this is just a symbol with a
value assigned, and so shows up as a base variable.

Similarly, the evaluator creates a symbol .Last.value which
appears as a variable in the base environment.

Errors can give rise to objects .Traceback and
last.warning in the base environment.

1.15 Modules

R makes use of a number of shared objects/DLLs stored in the
modules directory. These are parts of the code which have been
chosen to be loaded ‘on demand’ rather than linked as dynamic libraries
or incorporated into the main executable/dynamic library.

For the remaining modules the motivation has been the amount of (often
optional) code they will bring in via libraries to which they are
linked.

internet

The internal HTTP and FTP clients and socket support, which link to
system-specific support libraries. This may load libcurl and on
Windows will load wininet.dll and ws2_32.dll.

lapack

The code which makes use of the LAPACK library, and is linked to
libRlapack or an external LAPACK library.

X11

(Unix-alikes only.) The X11(), jpeg(), png() and
tiff() devices. These are optional, and links to some or all of
the X11, pango, cairo, jpeg, libpng
and libtiff libraries.

1.16 Visibility

1.16.1 Hiding C entry points

We make use of the visibility mechanisms discussed in
Controlling visibility in Writing R Extensions,
C entry points not needed outside the main R executable/dynamic
library (and in particular in no package nor module) should be prefixed
by attribute_hidden.
Minimizing the visibility of symbols in the R dynamic library will
speed up linking to it (which packages will do) and reduce the
possibility of linking to the wrong entry points of the same name. In
addition, on some platforms reducing the number of entry points allows
more efficient versions of PIC to be used: somewhat over half the entry
points are hidden. A convenient way to hide variables (as distinct from
functions) is to declare them extern0 in header file Defn.h.

The visibility mechanism used is only available with some compilers and
platforms, and in particular not on Windows, where an alternative
mechanism is used. Entry points will not be made available in
R.dll if they are listed in the file
src/gnuwin32/Rdll.hide.
Entries in that file start with a space and must be strictly in
alphabetic order in the C locale (use sort on the file to
ensure this if you change it). It is possible to hide Fortran as well
as C entry points via this file: the former are lower-cased and have an
underline as suffix, and the suffixed name should be included in the
file. Some entry points exist only on Windows or need to be visible
only on Windows, and some notes on these are provided in file
src/gnuwin32/Maintainters.notes.

Because of the advantages of reducing the number of visible entry
points, they should be declared attribute_hidden where possible.
Note that this only has an effect on a shared-R-library build, and so
care is needed not to hide entry points that are legitimately used by
packages. So it is best if the decision on visibility is made when a
new entry point is created, including the decision if it should be
included in header file Rinternals.h. A list of the visible
entry points on shared-R-library build on a reasonably standard
Unix-alike can be made by something like

1.16.2 Variables in Windows DLLs

Windows is unique in that it conventionally treats importing variables
differently from functions: variables that are imported from a DLL need
to be specified by a prefix (often ‘_imp_’) when being linked to
(‘imported’) but not when being linked from (‘exported’). The details
depend on the compiler system, and have changed for MinGW during the
lifetime of that port. They are in the main hidden behind some macros
defined in header file R_ext/libextern.h.

A (non-function) variable in the main R sources that needs to be
referred to outside R.dll (in a package, module or another DLL
such as Rgraphapp.dll) should be declared with prefix
LibExtern. The main use is in Rinternals.h, but it needs
to be considered for any public header and also Defn.h.

It would nowadays be possible to make use of the ‘auto-import’ feature
of the MinGW port of ld to fix up imports from DLLs (and if
R is built for the Cygwin platform this is what happens). However,
this was not possible when the MinGW build of R was first constructed
in ca 1998, allows less control of visibility and would not work for
other Windows compiler suites.

It is only possible to check if this has been handled correctly by
compiling the R sources on Windows.

1.17 Lazy loading

Lazy loading is always used for code in packages but is optional
(selected by the package maintainer) for datasets in packages. When a
package/namespace which uses it is loaded, the package/namespace
environment is populated with promises for all the named objects: when
these promises are evaluated they load the actual code from a database.

There are separate databases for code and data, stored in the R
and data subdirectories. The database consists of two files,
name.rdb and name.rdx. The .rdb file
is a concatenation of serialized objects, and the .rdx file
contains an index. The objects are stored in (usually) a
gzip-compressed format with a 4-byte header giving the
uncompressed serialized length (in XDR, that is big-endian, byte order)
and read by a call to the primitive lazyLoadDBfetch. (Note that
this makes lazy-loading unsuitable for really large objects: the
unserialized length of an R object can exceed 4GB.)

The index or ‘map’ file name.rdx is a compressed serialized
R object to be read by readRDS. It is a list with three
elements variables, references and compressed. The
first two are named lists of integer vectors of length 2 giving the
offset and length of the serialized object in the name.rdb
file. Element variables has an entry for each named object:
references serializes a temporary environment used when named
environments are added to the database. compressed is a logical
indicating if the serialized objects were compressed: compression is
always used nowadays. We later added the values compressed = 2
and 3 for bzip2 and xz compression (with the
possibility of future expansion to other methods): these formats add a
fifth byte to the header for the type of compression, and store
serialized objects uncompressed if compression expands them.

The loader for a lazy-load database of code or data is function
lazyLoad in the base package, but note that there is a
separate copy to load base itself in file
R_HOME/base/R/base.

Lazy-load databases are created by the code in
src/library/tools/R/makeLazyLoad.R: the main tool is the
unexported function makeLazyLoadDB and the insertion of database
entries is done by calls to .Call("R_lazyLoadDBinsertValue",
...).

Lazy-load databases of less than 10MB are cached in memory at first use:
this was found necessary when using file systems with high latency
(removable devices and network-mounted file systems on Windows).

Lazy-load databases are loaded into the exports for a package, but not
into the namespace environment itself. Thus they are visible when the
package is attached, and also via the :: operator.
This was a deliberate design decision, as packages mostly make datasets
available for use by the end user (or other packages), and they should
not be found preferentially from functions in the package, surprising
users who expected the normal search path to be used. (There is an
alternative mechanism, sysdata.rda, for ‘system datasets’ that
are intended primarily to be used within the package.)

The same database mechanism is used to store parsed Rd files.
One or all of the parsed objects is fetched by a call to
tools:::fetchRdDB.

2 .Internal vs .Primitive

C code compiled into R at build time can be called directly in what
are termed primitives or via the .Internal interface,
which is very similar to the .External interface except in
syntax. More precisely, R maintains a table of R function names and
corresponding C functions to call, which by convention all start with
‘do_’ and return a SEXP. This table (R_FunTab in
file src/main/names.c) also specifies how many arguments to a
function are required or allowed, whether or not the arguments are to be
evaluated before calling, and whether the function is ‘internal’ in
the sense that it must be accessed via the .Internal interface,
or directly accessible in which case it is printed in R as
.Primitive.

Functions using .Internal() wrapped in a closure are in general
preferred as this ensures standard handling of named and default
arguments. For example, grep is defined as

and the use of as.character allows methods to be dispatched (for
example, for factors).

However, for reasons of convenience and also efficiency (as there is
some overhead in using the .Internal interface wrapped in a
function closure), the primitive functions are exceptions that can be
accessed directly. And of course, primitive functions are needed for
basic operations—for example .Internal is itself a primitive.
Note that primitive functions make no use of R code, and hence are
very different from the usual interpreted functions. In particular,
formals and body return NULL for such objects, and
argument matching can be handled differently. For some primitives
(including call, switch, .C and .subset)
positional matching is important to avoid partial matching of the first
argument.

The list of primitive functions is subject to change; currently, it
includes the following.

“Special functions” which really are language elements, but
implemented as primitive functions:

{ ( if for while repeat break next
return function quote switch

Language elements and basic operators (i.e., functions usually
not called as foo(a, b, ...)) for subsetting, assignment,
arithmetic, comparison and logic:

Note that optimizing NAMED = 1 is only effective within a
primitive (as the closure wrapper of a .Internal will set
NAMED = 2 when the promise to the argument is evaluated) and
hence replacement functions should where possible be primitive to avoid
copying (at least in their default methods).

intentionally use positional matching, and need to do so to avoid
partial matching to their first argument. They do check that the first
argument is unnamed or for the first two, partially matches the formal
argument name. On the other hand,

All the one-argument primitives check that if they are called with a
named argument that this (partially) matches the name given in the
documentation: this is also done for replacement functions with one
argument plus value.

The net effect is that argument matching for primitives intended for
end-user use as functions is done in the same way as for
interpreted functions except for the six exceptions where positional
matching is required.

2.1 Special primitives

A small number of primitives are specials rather than
builtins, that is they are entered with unevaluated arguments.
This is clearly necessary for the language constructs and the assignment
operators, as well as for && and || which conditionally
evaluate their second argument, and ~, .Internal,
call, expression, missing, on.exit,
quote and substitute which do not evaluate some of their
arguments.

rep and seq.int are special as they evaluate some of their
arguments conditional on which are non-missing.

log, round and signif are special to allow default
values to be given to missing arguments.

The subsetting, subassignment and @ operators are all special.
(For both extraction and replacement forms, $ and @
take a symbol argument, and [ and [[ allow missing
arguments.)

UseMethod is special to avoid the additional contexts added to
calls to builtins.

2.3 Prototypes for primitives

Prototypes are available for the primitive functions and operators, and
these are used for printing, args and package checking (e.g. by
tools::checkS3methods and by package codetools). There are
two environments in the base package (and namespace),
‘.GenericArgsEnv’ for those primitives which are internal S3
generics, and ‘.ArgsEnv’ for the rest. Those environments contain
closures with the same names as the primitives, formal arguments derived
(manually) from the help pages, a body which is a suitable call to
UseMethod or NULL and environment the base namespace.

The C code for print.default and args uses the closures in
these environments in preference to the definitions in base (as
primitives).

The QC function undoc checks that all the functions prototyped in
these environments are currently primitive, and that the primitives not
included are better thought of as language elements (at the time of
writing

). One could argue about ~, but it is known to the parser and has
semantics quite unlike a normal function. And : is documented
with different argument names in its two meanings.)

The QC functions codoc and checkS3methods also make use of
these environments (effectively placing them in front of base in the
search path), and hence the formals of the functions they contain are
checked against the help pages by codoc. However, there are two
problems with the generic primitives. The first is that many of the
operators are part of the S3 group generic Ops and that defines
their arguments to be e1 and e2: although it would be very
unusual, an operator could be called as e.g. "+"(e1=a, e2=b)
and if method dispatch occurred to a closure, there would be an argument
name mismatch. So the definitions in environment .GenericArgsEnv
have to use argument names e1 and e2 even though the
traditional documentation is in terms of x and y:
codoc makes the appropriate adjustment via
tools:::.make_S3_primitive_generic_env. The second discrepancy
is with the Math group generics, where the group generic is
defined with argument list (x, ...), but most of the members only
allow one argument when used as the default method (and round and
signif allow two as default methods): again fix-ups are used.

Those primitives which are in .GenericArgsEnv are checked (via
tests/primitives.R) to be generic via defining methods for
them, and a check is made that the remaining primitives are probably not
generic, by setting a method and checking it is not dispatched to (but
this can fail for other reasons). However, there is no certain way to
know that if other .Internal or primitive functions are not
internally generic except by reading the source code.

2.4 Adding a primitive

[For R-core use: reverse this procedure to remove a primitive. Most
commonly this is done by changing a .Internal to a primitive or
vice versa.]

Primitives are listed in the table R_FunTab in
src/main/names.c: primitives have ‘Y = 0’ in the ‘eval’
field.

There needs to be an ‘\alias’ entry in a help file in the base
package, and the primitive needs to be added to one of the lists at the
start of this section.

Some primitives are regarded as language elements (the current ones are
listed above). These need to be added to two lists of exceptions,
langElts in undoc() (in file
src/library/tools/R/QC.R) and lang_elements in
tests/primitives.R.

All other primitives are regarded as functions and should be listed in
one of the environments defined in src/library/base/R/zzz.R,
either .ArgsEnv or .GenericArgsEnv: internal generics also
need to be listed in the character vector .S3PrimitiveGenerics.
Note too the discussion about argument matching above: if you add a
primitive function with more than one argument by converting a
.Internal you need to add argument matching to the C code, and
for those with a single argument, add argument-name checking.

Do ensure that make check-devel has been run: that tests most
of these requirements.

3 Internationalization in the R sources

The process of marking messages (errors, warnings etc) for translation
in an R package is described in
Internationalization in Writing R Extensions,
and the standard packages included with R have (with an exception in
grDevices for the menus of the windows() device) been
internationalized in the same way as other packages.

3.1 R code

Internationalization for R code is done in exactly the same way as
for extension packages. As all standard packages which have R code
also have a namespace, it is never necessary to specify domain,
but for efficiency calls to message, warning and
stop should include domain = NA when the message is
constructed viagettextf, gettext or
ngettext.

For each package, the extracted messages and translation sources are
stored under package directory po in the source package, and
compiled translations under inst/po for installation to package
directory po in the installed package. This also applies to C
code in packages.

3.2 Main C code

The main C code (e.g. that in files src/*/*.c and in
the modules) is where R is closest to the sort of application for
which ‘gettext’ was written. Messages in the main C code are in
domain R and stored in the top-level directory po with
compiled translations under share/locale.

The list of files covered by the R domain is specified in file
po/POTFILES.in.

The normal way to mark messages for translation is via _("msg")
just as for packages. However, sometimes one needs to mark passages for
translation without wanting them translated at the time, for example
when declaring string constants. This is the purpose of the N_
macro, for example

3.3 Windows-GUI-specific code

Messages for the Windows GUI are in a separate domain ‘RGui’. This
was done for two reasons:

The translators for the Windows version of R might be separate from
those for the rest of R (familiarity with the GUI helps), and

Messages for Windows are most naturally handled in the native charset
for the language, and in the case of CJK languages the charset is
Windows-specific. (It transpires that as the iconv we ported
works well under Windows, this is less important than anticipated.)

Messages for the ‘RGui’ domain are marked by G_("msg"), a
macro that is defined in header file src/gnuwin32/win-nls.h. The
list of files that are considered is hardcoded in the
RGui.pot-update target of file po/Makefile.in.in: note
that this includes devWindows.c as the menus on the
windows device are considered to be part of the GUI. (There is
also GN_("msg"), the analogue of N_("msg").)

The template and message catalogs for the ‘RGui’ domain are in the
top-level po directory.

4 Structure of an Installed Package

The structure of a source packages is described in Creating R packages in Writing R Extensions: this
chapter is concerned with the structure of installed packages.

An installed package has a top-level file DESCRIPTION, a copy of
the file of that name in the package sources with a ‘Built’ field
appended, and file INDEX, usually describing the objects on which
help is available, a file NAMESPACE if the package has a name
space, optional files such as CITATION, LICENCE and
NEWS, and any other files copied in from inst. It will
have directories Meta, help and html (even if the
package has no help pages), almost always has a directory R and
often has a directory libs to contain compiled code. Other
directories with known meaning to R are data, demo,
doc and po.

Function library looks for a namespace and if one is found
passes control to loadNamespace. Then library or
loadNamespace looks for file R/pkgname, warns if it
is not found and otherwise sources the code (using sys.source)
into the package’s environment, then lazy-loads a database
R/sysdata if present. So how R code gets loaded depends on
the contents of R/pkgname: a standard template to load
lazy-load databases are provided in share/R/nspackloader.R.

Compiled code is usually loaded when the package’s namespace is loaded
by a useDynlib directive in a NAMESPACE file or by the
package’s .onLoad function. Conventionally compiled code is
loaded by a call to library.dynam and this looks in directory
libs (and in an appropriate sub-directory if sub-architectures
are in use) for a shared object (Unix-alike) or DLL (Windows).

Subdirectory data serves two purposes. In a package using
lazy-loading of data, it contains a lazy-load database Rdata,
plus a file Rdata.rds which contain a named character vector used
by data() in the (unusual) event that it is used for such a
package. Otherwise it is a copy of the data directory in the
sources, with saved images re-compressed if R CMD INSTALL
--resave-data was used.

Subdirectory demo supports the demo function, and is
copied from the sources.

4.1 Metadata

Directory Meta contains several files in .rds format, that
is serialized R objects written by saveRDS. All packages
have files Rd.rds, hsearch.rds, links.rds and
package.rds. Packages with namespaces have a file
nsInfo.rds, and those with data, demos or vignettes have
data.rds, demo.rds or vignette.rds files.

The structure of these files (and their existence and names) is private
to R, so the description here is for those trying to follow the R
sources: there should be no reference to these files in non-base
packages.

File package.rds is a dump of information extracted from the
DESCRIPTION file. It is a list of several components. The
first, ‘DESCRIPTION’, is a character vector, the DESCRIPTION
file as read by read.dcf. Further elements ‘Depends’,
‘Suggests’, ‘Imports’, ‘Rdepends’ and ‘Rdepends2’
record the ‘Depends’, ‘Suggests’ and ‘Imports’ fields.
These are all lists, and can be empty. The first three have an entry
for each package named, each entry being a list of length 1 or 3, which
element ‘name’ (the package name) and optional elements ‘op’
(a character string) and ‘version’ (an object of class
‘"package_version"’). Element ‘Rdepends’ is used for the
first version dependency on R, and ‘Rdepends2’ is a list of zero
or more R version dependencies—each is a three-element list of the
form described for packages. Element ‘Rdepends’ is no longer used,
but it is still potentially needed so R < 2.7.0 can detect that the
package was not installed for it.

File nsInfo.rds records a list, a parsed version of the
NAMESPACE file.

File Rd.rds records a data frame with one row for each help file.
The columns are ‘File’ (the file name with extension), ‘Name’
(the ‘\name’ section), ‘Type’ (from the optional
‘\docType’ section), ‘Title’, ‘Encoding’, ‘Aliases’,
‘Concepts’ and ‘Keywords’. All columns are character vectors
apart from ‘Aliases’, which is a list of character vectors.

File hsearch.rds records the information to be used by
‘help.search’. This is a list of four unnamed elements which are
character matrices for help files, aliases, keywords and concepts. All
the matrices have columns ‘ID’ and ‘Package’ which are used to
tie the aliases, keywords and concepts (the remaining column of the last
three elements) to a particular help file. The first element has
further columns ‘LibPath’ (stored as "" and filled in what
the file is loaded), ‘name’, ‘title’, ‘topic’ (the first
alias, used when presenting the results as
‘pkgname::topic’) and ‘Encoding’.

File links.rds records a named character vector, the names being
aliases and the values character strings of the form

"../../pkgname/html/filename.html"

File data.rds records a two-column character matrix with columns
of dataset names and titles from the corresponding help file. File
demo.rds has the same structure for package demos.

File vignette.rds records a dataframe with one row for each
‘vignette’ (.[RS]nw file in inst/doc) and with columns
‘File’ (the full file path in the sources), ‘Title’,
‘PDF’ (the pathless file name of the installed PDF version, if
present), ‘Depends’, ‘Keywords’ and ‘R’ (the pathless
file name of the installed R code, if present).

4.2 Help

All installed packages, whether they had any .Rd files or not,
have help and html directories. The latter normally only
contains the single file 00Index.html, the package index which
has hyperlinks to the help topics (if any).

Directory help contains files AnIndex, paths.rds
and pkgname.rd[bx]. The latter two files are a lazy-load
database of parsed .Rd files, accessed by
tools:::fetchRdDB. File paths.rds is a saved character
vector of the original path names of the .Rd files, used when
updating the database.

File AnIndex is a two-column tab-delimited file: the first column
contains the aliases defined in the help files and the second the
basename (without the .Rd or .rd extension) of the file
containing that alias. It is read by utils:::index.search to
search for files matching a topic (alias), and read by scan in
utils:::matchAvailableTopics, part of the completion system.

File aliases.rds is the same information as AnIndex as a
named character vector (names the topics, values the file basename), for
faster access.

5 Files

R provides many functions to work with files and directories: many of
these have been added relatively recently to facilitate scripting in
R and in particular the replacement of Perl scripts by R scripts
in the management of R itself.

These functions are implemented by standard C/POSIX library calls,
except on Windows. That means that filenames must be encoded in the
current locale as the OS provides no other means to access the file
system: increasingly filenames are stored in UTF-8 and the OS will
translate filenames to UTF-8 in other locales. So using a UTF-8 locale
gives transparent access to the whole file system.

Windows is another story. There the internal view of filenames is in
UTF-16LE (so-called ‘Unicode’), and standard C library calls can only
access files whose names can be expressed in the current codepage. To
circumvent that restriction, there is a parallel set of Windows-specific
calls which take wide-character arguments for filepaths. Much of the
file-handling in R has been moved over to using these functions, so
filenames can be manipulated in R as UTF-8 encoded character strings,
converted to wide characters (which on Windows are UTF-16LE) and passed
to the OS. The utilities RC_fopen and filenameToWchar
help this process. Currently file.copy to a directory,
list.files, list.dirs and path.expand work only
with filepaths encoded in the current codepage.

All these functions do tilde expansion, in the same way as
path.expand, with the deliberate exception of Sys.glob.

File names may be case sensitive or not: the latter is the norm on
Windows and OS X, the former on other Unix-alikes. Note that this
is a property of both the OS and the file system: it is often possible
to map names to upper or lower case when mounting the file system. This
can affect the matching of patterns in list.files and
Sys.glob.

File names commonly contain spaces on Windows and OS X but not
elsewhere. As file names are handled as character strings by R,
spaces are not usually a concern unless file names are passed to other
process, e.g. by a system call.

Windows has another couple of peculiarities. Whereas a POSIX file
system has a single root directory (and other physical file systems are
mounted onto logical directories under that root), Windows has separate
roots for each physical or logical file system (‘volume’), organized
under drives (with file paths starting D: for an
ASCII letter, case-insensitively) and network shares
(with paths like \netname\topdir\myfiles\a file. There is a
current drive, and path names without a drive part are relative to the
current drive. Further, each drive has a current directory, and
relative paths are relative to that current directory, on a particular
drive if one is specified. So D:dir\file and D: are valid
path specifications (the last being the current directory on drive
D:).

6 Graphics

R’s graphics internals were re-designed to enable multiple graphics
systems to be installed on top on the graphics ‘engine’ – currently
there are two such systems, one supporting ‘base’ graphics (based on
that in S and whose R code15 is in package
graphics) and one implemented in package grid.

At the lowest level is a graphics device, which manages a plotting
surface (a screen window or a representation to be written to a file).
This implements a set of graphics primitives, to ‘draw’

a circle, optionally filled

a rectangle, optionally filled

a line

a set of connected lines

a polygon, optionally filled

a paths, optionally filled using a winding rule

text

a raster image (optional)

and to set a clipping rectangle

as well as requests for information such as

the width of a string if plotted

the metrics (width, ascent, descent) of a single character

the current size of the plotting surface

and requests/opportunities to take action such as

start a new ‘page’, possibly after responding to a request to ask
the user for confirmation.

return the position of the device pointer (if any).

when a device become the current device or stops being the current
device (this is usually used to change the window title on a screen
device).

when drawing starts or finishes (e.g. used to flush graphics to
the screen when drawing stops).

wait for an event, for example a mouse click or keypress.

an ‘onexit’ action, to clean up if plotting is interrupted (by an
error or by the user).

capture the current contents of the device as a raster image.

close the device.

The device also sets a number of variables, mainly Boolean flags
indicating its capabilities. Devices work entirely in ‘device units’
which are up to its developer: they can be in pixels, big points (1/72
inch), twips, …, and can differ16 in the
‘x’ and ‘y’ directions.

The next layer up is the graphics ‘engine’ that is the main interface to
the device (although the graphics subsystems do talk directly to
devices). This is responsible for clipping lines, rectangles and
polygons, converting the pch values 0...26 to sets of
lines/circles, centring (and otherwise adjusting) text, rendering
mathematical expressions (‘plotmath’) and mapping colour descriptions
such as names to the internal representation.

Another function of the engine is to manage display lists and snapshots.
Some but not all instances of graphics devices maintain display lists, a
‘list’ of operations that have been performed on the device to produce
the current plot (since the device was opened or the plot was last
cleared, e.g. by plot.new). Screen devices generally maintain
a display list to handle repaint and resize events whereas file-based
formats do not—display lists are also used to implement
dev.copy() and friends. The display list is a pairlist of
.Internal (base graphics) or .Call.graphics (grid
graphics) calls, which means that the C code implementing a graphics
operation will be re-called when the display list is replayed: apart
from the part which records the operation if successful.

Snapshots of the current graphics state are taken by
GEcreateSnapshot and replayed later in the session by
GEplaySnapshot. These are used by recordPlot(),
replayPlot() and the GUI menus of the windows() device.
The ‘state’ includes the display list.

The top layer comprises the graphics subsystems. Although there is
provision for 24 subsystems since about 2001, currently still only two
exist, ‘base’ and
‘grid’. The base subsystem is registered with the engine when R is
initialized, and unregistered (via KillAllDevices) when an R
session is shut down. The grid subsystem is registered in its
.onLoad function and unregistered in the .onUnload
function. The graphics subsystem may also have ‘state’ information
saved in a snapshot (currently base does and grid does not).

Package grDevices was originally created to contain the basic
graphics devices (although X11 is in a separate load-on-demand
module because of the volume of external libraries it brings in). Since
then it has been used for other functionality that was thought desirable
for use with grid, and hence has been transferred from package
graphics to grDevices. This is principally concerned with
the handling of colours and recording and replaying plots.

6.1 Graphics Devices

R ships with several graphics devices, and there is support for
third-party packages to provide additional devices—several packages
now do. This section describes the device internals from the viewpoint
of a would-be writer of a graphics device.

6.1.1 Device structures

There are two types used internally which are pointers to structures
related to graphics devices.

The DevDesc type is a structure defined in the header file
R_ext/GraphicsDevice.h (which is included by
R_ext/GraphicsEngine.h). This describes the physical
characteristics of a device, the capabilities of the device driver and
contains a set of callback functions that will be used by the graphics
engine to obtain information about the device and initiate actions
(e.g. a new page, plotting a line or some text). Type pDevDesc
is a pointer to this type.

The following callbacks can be omitted (or set to the null pointer,
their default value) when appropriate default behaviour will be taken by
the graphics engine: activate, cap, deactivate,
locator, holdflush (API version 9), mode,
newFrameConfirm, path, raster and size.

The relationship of device units to physical dimensions is set by the
element ipr of the DevDesc structure: a ‘double’
array of length 2.

The GEDevDesc type is a structure defined in
R_ext/GraphicsEngine.h (with comments in the file) as

So this is essentially a device structure plus information about the
device maintained by the graphics engine and normally17 visible to the engine
and not to the device. Type pGEDevDesc is a pointer to this
type.

The graphics engine maintains an array of devices, as pointers to
GEDevDesc structures. The array is of size 64 but the first
element is always occupied by the "null device" and the final
element is kept as NULL as a sentinel.18 This array is reflected in the R variable
‘.Devices’. Once a device is killed its element becomes available
for reallocation (and its name will appear as "" in
‘.Devices’). Exactly one of the devices is ‘active’: this is the
the null device if no other device has been opened and not killed.

Each instance of a graphics device needs to set up a GEDevDesc
structure by code very similar to

The DevDesc structure contains a void * pointer
‘deviceSpecific’ which is used to store data specific to the
device. Setting up the device driver includes initializing all the
non-zero elements of the DevDesc structure.

Note that the device structure is zeroed when allocated: this provides
some protection against future expansion of the structure since the
graphics engine can add elements that need to be non-NULL/non-zero to be
‘on’ (and the structure ends with 64 reserved bytes which will be zeroed
and allow for future expansion).

Rather more protection is provided by the version number of the
engine/device API, R_GE_version defined in
R_ext/GraphicsEngine.h together with access functions

int R_GE_getVersion(void);
void R_GE_checkVersionOrDie(int version);

If a graphics device calls R_GE_checkVersionOrDie(R_GE_version)
it can ensure it will only be used in versions of R which provide the
API it was designed for and compiled against.

6.1.2 Device capabilities

The following ‘capabilities’ can be defined for the device’s
DevDesc structure.

canChangeGamma –
Rboolean: can the display gamma be adjusted? This is now
ignored, as gamma support has been removed.

canHadj –
integer: can the device do horizontal adjustment of text
via the text callback, and if so, how precisely? 0 = no
adjustment, 1 = {0, 0.5, 1} (left, centre, right justification) or 2 =
continuously variable (in [0,1]) between left and right justification.

canGenMouseDown –
Rboolean: can the device handle mouse down events? This
flag and the next three are not currently used by R, but are maintained
for back compatibility.

canGenMouseMove –
Rboolean: ditto for mouse move events.

canGenMouseUp –
Rboolean: ditto for mouse up events.

canGenKeybd –
Rboolean: ditto for keyboard events.

hasTextUTF8 –
Rboolean: should non-symbol text be sent (in UTF-8) to the
textUTF8 and strWidthUTF8 callbacks, and sent as Unicode
points (negative values) to the metricInfo callback?

wantSymbolUTF8 –
Rboolean: should symbol text be handled in UTF-8 in the same way
as other text? Requires textUTF8 = TRUE.

haveTransparency:
does the device support semi-transparent colours?

haveTransparentBg:
can the background be fully or semi-transparent?

haveRaster:
is there support for rendering raster images?

haveCapture:
is there support for grid::grid.cap?

haveLocator:
is there an interactive locator?

The last three can often be deduced to be false from the presence of
NULL entries instead of the corresponding functions.

6.1.3 Handling text

Handling text is probably the hardest task for a graphics device, and
the design allows for the device to optionally indicate that it has
additional capabilities. (If the device does not, these will if
possible be handled in the graphics engine.)

The three callbacks for handling text that must be in all graphics
devices are text, strWidth and metricInfo with
declarations

The ‘gc’ parameter provides the graphics context, most importantly
the current font and fontsize, and ‘dd’ is a pointer to the active
device’s structure.

The text callback should plot ‘str’ at ‘(x,
y)’19 with an anti-clockwise rotation of
‘rot’ degrees. (For ‘hadj’ see below.) The interpretation
for horizontal text is that the baseline is at y and the start is
a x, so any left bearing for the first character will start at
x.

The strWidth callback computes the width of the string which it
would occupy if plotted horizontally in the current font. (Width here
is expected to include both (preferably) or neither of left and right
bearings.)

The metricInfo callback computes the size of a single
character: ascent is the distance it extends above the baseline
and descent how far it extends below the baseline.
width is the amount by which the cursor should be advanced when
the character is placed. For ascent and descent this is
intended to be the bounding box of the ‘ink’ put down by the glyph and
not the box which might be used when assembling a line of conventional
text (it needs to be for e.g. hat(beta) to work correctly).
However, the width is used in plotmath to advance to the next
character, and so needs to include left and right bearings.

The interpretation of ‘c’ depends on the locale. In a
single-byte locale values 32...255 indicate the corresponding
character in the locale (if present). For the symbol font (as used by
‘graphics::par(font=5)’, ‘grid::gpar(fontface=5’) and by
‘plotmath’), values 32...126, 161...239, 241...254 indicate
glyphs in the Adobe Symbol encoding. In a multibyte locale, c
represents a Unicode point (except in the symbol font). So the function
needs to include code like

In addition, if device capability hasTextUTF8 (see below) is
true, Unicode points will be passed as negative values: the code snippet
above shows how to handle this. (This applies to the symbol font only
if device capability wantSymbolUTF8 is true.)

If possible, the graphics device should handle clipping of text. It
indicates this by the structure element canClip which if true
will result in calls to the callback clip to set the clipping
region. If this is not done, the engine will clip very crudely (by
omitting any text that does not appear to be wholly inside the clipping
region).

The device structure has an integer element canHadj, which
indicates if the device can do horizontal alignment of text. If this is
one, argument ‘hadj’ to text will be called as 0 ,0.5,
1 to indicate left-, centre- and right-alignment at the indicated
position. If it is two, continuous values in the range [0, 1]
are assumed to be supported.

Capability hasTextUTF8 if true, it has two consequences.
First, there are callbacks textUTF8 and strWidthUTF8 that
should behave identically to text and strWidth except that
‘str’ is assumed to be in UTF-8 rather than the current locale’s
encoding. The graphics engine will call these for all text except in
the symbol font. Second, Unicode points will be passed to the
metricInfo callback as negative integers. If your device would
prefer to have UTF-8-encoded symbols, define wantSymbolUTF8 as
well as hasTextUTF8. In that case text in the symbol font is
sent to textUTF8 and strWidthUTF8.

Some devices can produce high-quality rotated text, but those based on
bitmaps often cannot. Those which can should set
useRotatedTextInContour to be true from graphics API version 4.

Several other elements relate to the precise placement of text by the
graphics engine:

These are more than a little mysterious. Element cra provides an
indication of the character size, par("cra") in base graphics, in
device units. The mystery is what is meant by ‘character size’: which
character, which font at which size? Some help can be obtained by
looking at what this is used for. The first element, ‘width’, is not
used by R except to set the graphical parameters. The second,
‘height’, is use to set the line spacing, that is the relationship
between par("mai") and par("mai") and so on. It is
suggested that a good choice is

dd->cra[0] = 0.9 * fnsize;
dd->cra[1] = 1.2 * fnsize;

where ‘fnsize’ is the ‘size’ of the standard font (cex=1)
on the device, in device units. So for a 12-point font (the usual
default for graphics devices), ‘fnsize’ should be 12 points in
device units.

The remaining elements are yet more mysterious. The postscript()
device says

It seems that xCharOffset is not currently used, and
yCharOffset is used by the base graphics system to set vertical
alignment in text() when pos is specified, and in
identify(). It is occasionally used by the graphic engine when
attempting exact centring of text, such as character string values of
pch in points() or grid.points()—however, it is
only used when precise character metric information is not available or
for multi-line strings.

yLineBias is used in the base graphics system in axis() and
mtext() to provide a default for their ‘padj’ argument.

6.1.4 Conventions

The aim is to make the (default) output from graphics devices as similar
as possible. Generally people follow the model of the postscript
and pdf devices (which share most of their internal code).

The following conventions have become established:

The default size of a device should be 7 inches square.

There should be a ‘pointsize’ argument which defaults to 12, and it
should give the pointsize in big points (1/72 inch). How exactly this
is interpreted is font-specific, but it should use a font which works
with lines packed 1/6 inch apart, and looks good with lines 1/5 inch
apart (that is with 2pt leading).

The default font family should be a sans serif font, e.g Helvetica or
similar (e.g. Arial on Windows).

lwd = 1 should correspond to a line width of 1/96 inch. This
will be a problem with pixel-based devices, and generally there is a
minimum line width of 1 pixel (although this may not be appropriate
where anti-aliasing of lines is used, and cairo prefers a minimum
of 2 pixels).

Even very small circles should be visible, e.g. by using a minimum
radius of 1 pixel or replacing very small circles by a single filled
pixel.

How RGB colour values will be interpreted should be documented, and
preferably be sRGB.

The help page should describe its policy on these conventions.

These conventions are less clear-cut for bitmap devices, especially
where the bitmap format does not have a design resolution.

The interpretation of the line texture (par("lty") is described
in the header GraphicsEngine.h and in the help for par: note that the
‘scale’ of the pattern should be proportional to the line width (at
least for widths above the default).

Since mode = 2 has only recently been documented at device level.
It could be used to change the graphics cursor, but devices currently do
that in the locator callback. (In base graphics the mode is set
for the duration of a locator call, but if type != "n" is
switched back for each point whilst annotation is being done.)

Many devices do indeed do nothing on this call, but some screen devices
ensure that drawing is flushed to the screen when called with mode
= 0. It is tempting to use it for some sort of buffering, but note
that ‘drawing’ is interpreted at quite a low level and a typical single
figure will stop and start drawing many times. The buffering introduced
in the X11() device makes use of mode = 0 to indicate
activity: it updates the screen after ca 100ms of inactivity.

6.1.6 Graphics events

Graphics devices may be designed to handle user interaction: not all are.

Users may use grDevices::setGraphicsEventEnv to set the
eventEnv environment in the device driver to hold event
handlers. When the user calls grDevices::getGraphicsEvent, R will
take three steps. First, it sets the device driver member
gettingEvent to true for each device with a
non-NULLeventEnv entry, and calls initEvent(dd,
true) if the callback is defined. It then enters an event loop. Each
time through the loop R will process events once, then check whether any
device has set the result member of eventEnv to a
non-NULL value, and will save the first such value found to be
returned. C functions doMouseEvent and doKeybd are
provided to call the R event handlers onMouseDown,
onMouseMove, onMouseUp, and onKeybd and set
eventEnv$result during this step. Finally, initEvent is
called again with init=false to inform the the devices that the
loop is done, and the result is returned to the user.

6.1.7 Specific devices

Specific devices are mostly documented by comments in their sources,
although for devices of many years’ standing those comments can be in
need of updating. This subsection is a repository of notes on design
decisions.

6.1.7.1 X11()

The X11(type="Xlib") device dates back to the mid 1990’s and was
written then in Xlib, the most basic X11 toolkit. It has since
optionally made use of a few features from other toolkits: libXt
is used to read X11 resources, and libXmu is used in the handling
of clipboard selections.

Using basic Xlib code makes drawing fast, but is limiting. There
is no support of translucent colours (that came in the Xrender
toolkit of 2000) nor for rotated text (which R implements by
rendering text to a bitmap and rotating the latter).

The hinting for the X11 window asks for backing store to be used, and
some windows managers may use it to handle repaints, but it seems that
most repainting is done by replaying the display list (and here the fast
drawing is very helpful).

There are perennial problems with finding fonts. Many users fail to
realize that fonts are a function of the X server and not of the machine
that R is running on. After many difficulties, R tries first to
find the nearest size match in the sizes provided for Adobe fonts in the
standard 75dpi and 100dpi X11 font packages—even that will fail to
work when users of near-100dpi screens have only the 75dpi set
installed. The 75dpi set allows sizes down to 6 points on a 100dpi
screen, but some users do try to use smaller sizes and even 6 and 8
point bitmapped fonts do not look good.

Introduction of UTF-8 locales has caused another wave of difficulties.
X11 has very few genuine UTF-8 fonts, and produces composite fontsets
for the iso10646-1 encoding. Unfortunately these seem to have
low coverage apart from a few monospaced fonts in a few sizes (which are
not suitable for graph annotation), and where glyphs are missing what is
plotted is often quite unsatisfactory.

The current approach is to make use of more modern toolkits, namely
cairo for rendering and Pango for font
management—because these are associated with Gtk+2 they are
widely available. Cairo supports translucent colours and alpha-blending
(viaXrender), and anti-aliasing for the display of lines
and text. Pango’s font management is based on fontconfig and
somewhat mysterious, but it seems mainly to use Type 1 and TrueType
fonts on the machine running R and send grayscale bitmaps to cairo.

6.1.7.2 windows()

The windows() device is a family of devices: it supports plotting
to Windows (enhanced) metafiles, BMP, JPEG, PNG and
TIFF files as well as to Windows printers.

In most of these cases the primary plotting is to a bitmap: this is used
for the (default) buffering of the screen device, which also enables the
current plot to be saved to BMP, JPEG, PNG or TIFF (it is the internal
bitmap which is copied to the file in the appropriate format).

The device units are pixels (logical ones on a metafile device).

The code was originally written by Guido Masarotto with extensive use of
macros, which can make it hard to disentangle.

For a screen device, xd->gawin is the canvas of the screen, and
xd->bm is the off-screen bitmap. So macro DRAW arranges
to plot to xd->bm, and if buffering is off, also to
xd->gawin. For all other device, xd->gawin is the canvas,
a bitmap for the jpeg() and png() device, and an internal
representation of a Windows metafile for the win.metafile() and
win.print device. Since ‘plotting’ is done by Windows GDI calls
to the appropriate canvas, its precise nature is hidden by the GDI
system.

Buffering on the screen device is achieved by running a timer, which
when it fires copies the internal bitmap to the screen. This is set to
fire every 500ms (by default) and is reset to 100ms after plotting
activity.

Repaint events are handled by copying the internal bitmap to the screen
canvas (and then reinitializing the timer), unless there has been a resize.
Resizes are handled by replaying the display list: this might not be
necessary if a fixed canvas with scrollbars is being used, but that is
the least popular of the three forms of resizing.

Text on the device has moved to ‘Unicode’ (UCS-2) in recent years.
UTF-8 is requested (hasTextUTF8 = TRUE) for standard text, and
converted to UCS-2 in the plotting functions in file
src/extra/graphapp/gdraw.c. However, GDI has no support for
Unicode symbol fonts, and symbols are handled in Adobe Symbol encoding.

There is support for translucent colours (with alpha channel between 0
and 255) was introduced on the screen device and bitmap
devices.20 This is done by drawing on a further internal bitmap,
xd->bm2, in the opaque version of the colour then alpha-blending
that bitmap to xd->bm. The alpha-blending routine is in a
separate DLL, msimg32.dll, which is loaded on first use. As
small a rectangular region as reasonably possible is alpha-blended (this
is rectangle r in the code), but things like mitre joins make
estimation of a tight bounding box too much work for lines and polygonal
boundaries. Translucent-coloured lines are not common, and the
performance seems acceptable.

The support for a transparent background in png() predates full
alpha-channel support in libpng (let alone in PNG viewers), so
makes use of the limited transparency support in earlier versions of
PNG. Where 24-bit colour is used, this is done by marking a single
colour to be rendered as transparent. R chose ‘#fdfefd’, and
uses this as the background colour (in GA_NewPage if the
specified background colour is transparent (and all non-opaque
background colours are treated as transparent). So this works by
marking that colour in the PNG file, and viewers without transparency
support see a slightly-off-white background, as if there were a
near-white canvas. Where a palette is used in the PNG file (if less
than 256 colours were used) then this colour is recorded with full
transparency and the remaining colours as opaque. If 32-bit colour were
available then we could add a full alpha channel, but this is dependent
on the graphics hardware and undocumented properties of GDI.

6.2 Colours

Devices receive colours as a typedefrcolor (an
unsigned int) defined in the header
R_ext/GraphicsEngine.h). The 4 bytes are R ,G,
B and alpha from least to most significant. So each of RGB
has 256 levels of luminosity from 0 to 255. The alpha byte represents
opacity, so value 255 is fully opaque and 0 fully transparent: many but
not all devices handle semi-transparent colours.

Colors can be created in C via the macro R_RGBA, and a set of
macros are defined in R_ext/GraphicsDevice.h to extract the
various components.

Colours in the base graphics system were originally adopted from S (and
before that the GRZ library from Bell Labs), with the concept of a
(variable-sized) palette of colours referenced by numbers
‘1...N’ plus ‘0’ (the background colour of the current
device). R introduced the idea of referring to colours by character
strings, either in the forms ‘#RRGGBB’ or ‘#RRGGBBAA’
(representing the bytes in hex) as given by function rgb() or via
names: the 657 known names are given in the character vector
colors and in a table in file colors.c in package
grDevices. Note that semi-transparent colours are not
‘premultiplied’, so 50% transparent white is ‘#ffffff80’.

Integer or character NA colours are mapped internally to
transparent white, as is the character string "NA".

The handling of negative colour numbers was undefined (and inconsistent)
prior to R 3.0.0, which made them an error. Colours greater than
‘N’ are wrapped around, so that for example with the default
palette of size 8, colour ‘10’ is colour ‘2’ in the palette.

Integer colours have been used more widely than the base graphics
sub-system, as they are supported by package grid and hence by
lattice and ggplot2. (They are also used by package
rgl.) grid did re-define colour ‘0’ to be
transparent white, but rgl used col2rgb and hence the
background colour of base graphics.

Note that positive integer colours refer to the current palette and
colour ‘0’ to the current device (and a device is opened if needs
be). These are mapped to type rcolor at the time of use: this
matters when re-playing the display list, e.g. when a device is
resized or dev.copy is used. The palette should be thought of as
per-session: it is stored in package grDevices.

The convention is that devices use the colorspace ‘sRGB’. This is an
industry standard: it is used by Web browsers and JPEGs from all but
high-end digital cameras. The interpretation is a matter for graphics
devices and for code that manipulates colours, but not for the graphics
engine or subsystems.

R uses a painting model similar to PostScript and PDF. This means
that where shapes (circles, rectangles and polygons) can both be filled
and have a stroked border, the fill should be painted first and then the
border (or otherwise only half the border will be visible). Where both
the fill and the border are semi-transparent there is some room for
interpretation of the intention. Most devices first paint the fill and
then the border, alpha-blending at each step. However, PDF does some
automatic grouping of objects, and when the fill and the border
have the same alpha, they are painted onto the same layer and then
alpha-blended in one step. (See p. 569 of the PDF Reference Sixth
Edition, version 1.7. Unfortunately, although this is what the PDF
standard says should happen, it is not correctly implemented by some
viewers.)

The mapping from colour numbers to type rcolor is primarily done
by function RGBpar3: this is exported from the R binary but
linked to code in package grDevices. The first argument is a
SEXP pointing to a character, integer or double vector, and the
second is the rcolor value for colour 0 (or "0").
C entry point RGBpar is a wrapper that takes 0 to be
transparent white: it is often used to set colour defaults for devices.
The R-level wrapper is col2rgb.

There is also R_GE_str2col which takes a C string and converts to
type rcolor: "0' is converted to transparent white.

There is a R-level conversion of colours to ‘##RRGGBBAA’ by
image.default(useRaster = TRUE).

The other color-conversion entry point in the API is name2col
which takes a colour name (a C string) and returns a value of type
rcolor. This handles "NA", "transparent" and the
657 colours known to the R function colors().

6.3 Base graphics

The base graphics system was migrated to package graphics in R
3.0.0: it was previously implemented in files in src/main.

For historical reasons it is largely implemented in two layers.
Files plot.c, plot3d.c and par.c contain the code
for the around 30 .External calls that implement the basic
graphics operations. This code then calls functions with names starting
with G and declared in header Rgraphics.h in file
graphics.c, which in turn call the graphics engine (whose
functions almost all have names starting with GE).

A large part of the infrastructure of the base graphics subsystem are
the graphics parameters (as set/read by par()). These are stored
in a GPar structure declared in the private header
Graphics.h. This structure has two variables (state and
valid) tracking the state of the base subsystem on the device,
and many variables recording the graphics parameters and functions of
them.

The base system state is contained in baseSystemState structure
defined in R_ext/GraphicsBase.h. This contains three GPar
structures and a Boolean variable used to record if plot.new()
(or persp) has been used successfully on the device.

The three copies of the GPar structure are used to store the
current parameters (accessed via gpptr), the ‘device copy’
(accessed via dpptr) and space for a saved copy of the ‘device
copy’ parameters. The current parameters are, clearly, those currently
in use and are copied from the ‘device copy’ whenever plot.new()
is called (whether or not that advances to the next ‘page’). The saved
copy keeps the state when the device was last completely cleared (e.g.
when plot.new() was called with par(new=TRUE)), and is
used to replay the display list.

The separation is not completely clean: the ‘device copy’ is altered if
a plot with log scale(s) is set up via plot.window().

There is yet another copy of most of the graphics parameters in
static variables in graphics.c which are used to preserve
the current parameters across the processing of inline parameters in
high-level graphics calls (handled by ProcessInlinePars).

Snapshots of the base subsystem record the ‘saved device copy’ of the
GPar structure.

6.3.1 Arguments and parameters

There is an unfortunate confusion between some of the graphical
parameters (as set by par) and arguments to base graphic
functions of the same name. This description may help set the record
straight.

Most of the high-level plotting functions accept graphical parameters as
additional arguments, which are then often passed to lower-level
functions if not already named arguments (which is the main source of
confusion).

Graphical parameter bg is the background colour of the plot.
Argument bg refers to the fill colour for the filled symbols
21 to 25. It is an argument to the function
plot.xy, but normally passed by the default method of
points, often from a plot method.

Graphics parameters cex, col, lty, lwd and
pch also appear as arguments of plot.xy and so are often
passed as arguments from higher-level plot functions such as
lines, points and plot methods. They appear as
arguments of legend, col, lty and lwd are
arguments of arrows and segments. When used as arguments
they can be vectors, recycled to control the various lines, points and
segments. When set a graphical parameters they set the default
rendering: in addition par(cex=) sets the overall character
expansion which subsequent calls (as arguments or on-line graphical
parameters) multiply.

The handling of missing values differs in the two classes of uses.
Generally these are errors when used in par but cause the
corresponding element of the plot to be omitted when used as an element
of a vector argument. Originally the interpretation of arguments was
mainly left to the device, but as from R 3.0.0 some of this is
pre-empted in the graphics engine (but for example the handling of
lwd = 0 remains device-specific, with some interpreting it as a
‘thinnest possible’ line).

6.4 Grid graphics

7 GUI consoles

The standard R front-ends are programs which run in a terminal, but
there are several ways to provide a GUI console.

This can be done by a package which is loaded from terminal-based R
and launches a console as part of its startup code or by the user
running a specific function: package Rcmdr is a well-known
example with a Tk-based GUI.

There used to be a Gtk-based console invoked by R --gui=GNOME:
this relied on special-casing in the front-end shell script to launch a
different executable. There still is R --gui=Tk, which starts
terminal-based R and runs tcltk::tkStartGui() as part of the
modified startup sequence.

However, the main way to run a GUI console is to launch a separate
program which runs embedded R: this is done by Rgui.exe on
Windows and R.app on OS X. The first is an integral part
of R and the code for the console is currently in R.dll.

7.1 R.app

R.app is a OS X application which provides a console. Its
sources are a separate project21, and its binaries
link to an R installation which it runs as a dynamic library
libR.dylib. The standard CRAN distribution of R for
OS X bundles the GUI and R itself, but installing the GUI is optional
and either component can be updated separately.

R.app relies on libR.dylib being in a specific place,
and hence on R having been built and installed as a Mac OS X
‘framework’. Specifically, it uses
/Library/Frameworks/R.framework/R. This is a symbolic link, as
frameworks can contain multiple versions of R. It eventually
resolves to
/Library/Frameworks/R.framework/Versions/Current/Resources/lib/libR.dylib,
which is (in the CRAN distribution) a ‘fat’ binary containing
multiple sub-architectures.

OS X applications are directory trees: each R.app contains
a front-end written in Objective-C for one sub-architecture: in the
standard distribution there are separate applications for 32- and 64-bit
Intel architectures.

Originally the R sources contained quite a lot of code used only by
the OS X GUI, but by R 3.0.0 this was been migrated to the
R.app sources.

R.app starts R as an embedded application with a
command-line which includes --gui=aqua (see below). It uses
most of the interface pointers defined in the header
Rinterface.h, plus a private interface pointer in file
src/main/sysutils.c. It adds an environment
it names tools:RGUI to the second position in the search path.
This contains a number of utility functions used to support the menu
items, for example package.manager(), plus functions q()
and quit() which mask those in package base—the custom
versions save the history in a way specific to R.app.

There is a configure option --with-aqua for R
which customizes the way R is built: this is distinct from the
--enable-R-framework option which causes make install
to install R as the framework needed for use with R.app. (The
option --with-aqua is the default on OS X.) It sets the
macro HAVE_AQUA in config.h and the make variable
BUILD_AQUA_TRUE. These have several consequences:

The quartz() device is built (other than as a stub) in package
grDevices: this needs an Objective-C compiler. Then
quartz() can be used with terminal R provided the latter has
access to the OS X screen.

File src/unix/aqua.c is compiled. This now only contains an
interface pointer for the quartz() device(s).

capabilities("aqua") is set to TRUE.

The default path for a personal library directory is set as
~/Library/R/x.y/library.

There is support for setting a ‘busy’ indicator whilst waiting for
system() to return.

R_ProcessEvents is inhibited in a forked child from package
parallel. The associated callback in R.app does things
which should not be done in a child, and forking forks the whole process
including the console.

There is support for starting the embedded R with the option
--gui=aqua: when this is done the global C variable
useaqua is set to a true value. This has consequences:

The R session is asserted to be interactive viaR_Interactive.

.Platform$GUI is set to "AQUA". That has consequences:

The environment variable DISPLAY is set to ‘:0’ if not
already set.

/usr/local/bin is appended to PATH since that is where
gfortran is installed.

The default HTML browser is switched to the one in R.app.

Various widgets are switched to the versions provided in
R.app: these include graphical menus, the data editor (but not
the data viewer used by View()) and the workspace browser invoked
by browseEnv().

The grDevices package when loaded knows that it is being run
under R.app and so informs any quartz devices that a
Quartz event loop is already running.

The use of the OS’s system function (including by system()
and system2(), and to launch editors and pagers) is replaced by a
version in R.app (which by default just calls the OS’s
system with various signal handlers reset).

If either R was started by --gui=aqua or R is running in
a terminal which is not of type ‘dumb’, the standard output to
files stdout and stderr is directed through the C function
Rstd_WriteConsoleEx. This uses ANSI terminal escapes to render
lines sent to stderr as bold on stdout.

For historical reasons the startup option -psn is allowed but
ignored. (It seems that in 2003, ‘r27492’, this was added by Finder.)

8 Tools

The behavior of R CMD check can be controlled through a
variety of command line arguments and environment variables.

There is an internal --install=value command line
argument not shown by R CMD check --help, with possible values

check:file

Assume that installation was already performed with stdout/stderr to
file, the contents of which need to be checked (without repeating
the installation). This is useful for checks applied by repository
maintainers: it reduces the check time by the installation time given
that the package has already been installed. In this case, one also
needs to specify where the package was installed to using command
line option --library.

Allows turning off checkFF() testing. If set to
‘registration’, checks the registration information (number of
arguments, correct choice of .C/.Fortran/.Call/.External) for
such calls provided the package is installed.
Default: true.

If set to a non-empty value, a space-separated list of repositories to
use to determine known packages. Default: empty, when the CRAN,
Omegahat and Bioconductor repositories known to R is used.

_R_CHECK_SRC_MINUS_W_IMPLICIT_

Control whether installation output is checked for compilation warnings
about implicit function declarations (as spotted by GCC with command
line option -Wimplicit-function-declaration, which is implied
by -Wall).
Default: false.

_R_CHECK_SRC_MINUS_W_UNUSED_

Control whether installation output is checked for compilation warnings
about unused code constituents (as spotted by GCC with command line
option -Wunused, which is implied by -Wall).
Default: true.

_R_CHECK_WALL_FORTRAN_

Control whether gfortran 4.0 or later -Wall warnings are used in
the analysis of installation output.
Default: false, even though the warnings are justifiable.

_R_CHECK_ASCII_CODE_

If true, check R code for non-ascii characters.
Default: true.

_R_CHECK_ASCII_DATA_

If true, check data for non-ascii characters.
Default: true.

_R_CHECK_COMPACT_DATA_

If true, check data for ascii and uncompressed saves, and also check if
using bzip2 or xz compression would be significantly
better.
Default: true.

_R_CHECK_SKIP_ARCH_

Comma-separated list of architectures that will be omitted from
checking in a multi-arch setup.
Default: none.

_R_CHECK_SKIP_TESTS_ARCH_

Comma-separated list of architectures that will be omitted from
running tests in a multi-arch setup.
Default: none.

_R_CHECK_SKIP_EXAMPLES_ARCH_

Comma-separated list of architectures that will be omitted from
running examples in a multi-arch setup.
Default: none.

Should du be used to find the installed sizes of packages?
R CMD check does check for the availability of du.
but this option allows the check to be overruled if an unsuitable
command is found (including one that does not respect the -k
flag to report in units of 1Kb, or reports in a different format – the
GNU, OS X and Solaris du commands have been tested).
Default: true if du is found.

_R_CHECK_DOC_SIZES_

Should qpdf be used to check the installed sizes of PDFs?
Default: true if qpdf is found.

_R_CHECK_DOC_SIZES2_

Should gs be used to check the installed sizes of PDFs? This
is slower than (and in addition to) the previous check, but does detect
figures with excessive detail (often hidden by over-plotting) or bitmap
figures with too high a resolution. Requires that R_GSCMD is set
to a valid program, or gs (or on Windows,
gswin32.exe or gswin64c.exe) is on the path.
Default: false (but true for CRAN submission checks).

_R_CHECK_ALWAYS_LOG_VIGNETTE_OUTPUT_

By default the output from running the R code in the vignettes is
kept only if there is an error.
Default: false.

_R_CHECK_CLEAN_VIGN_TEST_

Should the vign_test directory be removed if the test is successful?
Default: true.

_R_CHECK_REPLACING_IMPORTS_

Should warnings about replacing imports be reported? These sometimes come
from auto-generated NAMESPACE files in other packages, but most
often from importing the whole of a namespace rather than using
importFrom.
Default: false (but true for CRAN submission checks).

_R_CHECK_UNSAFE_CALLS_

Check for calls that appear to tamper with (or allow tampering with)
already loaded code not from the current package: such calls may well
contravene CRAN policies.
Default: true.

_R_CHECK_TIMINGS_

Optionally report timings for installation, examples, tests and
running/re-building vignettes as part of the check log. The format is
‘[as/bs]’ for the total CPU time (including child processes)
‘a’ and elapsed time ‘b’, except on Windows, when it is
‘[bs]’. In most cases timings are only given for ‘OK’ checks.
Times with an elapsed component over 10 mins are reported in minutes
(with abbreviation ‘m’). The value is the smallest numerical value
in elapsed seconds that should be reported: non-numerical values
indicate that no report is required, a value of ‘0’ that a report
is always required.
Default: "". (10 for CRAN checks.)

_R_CHECK_INSTALL_DEPENDS_

If set to a true value and a test installation is to be done, this is
done with .libPaths() containing just a temporary library
directory and .Library. The temporary library is populated by
symbolic links22
to the installed copies of all the Depends/Imports/LinkingTo packages
which are not in .Library. Default: false (but true for CRAN
submission checks).

Note that this is actually implemented in R CMD INSTALL, so it
is available to those who first install recording to a log, then call
R CMD check.

_R_CHECK_DEPENDS_ONLY_

_R_CHECK_SUGGESTS_ONLY_

If set to a true value, running examples, tests and vignettes is done
with .libPaths() containing just a temporary library directory
and .Library. The temporary library is populated by symbolic
links23 to the installed copies of
all the Depends/Imports and (for the second only) Suggests packages
which are not in .Library. (As an exception, packages in a
‘VignetteBuilder’ field are always made available.)
Default: false (but _R_CHECK_SUGGESTS_ONLY_ is true for CRAN checks).

_R_CHECK_NO_RECOMMENDED_

If set to a true value, augment the previous checks to make recommended
packages unavailable unless declared.
Default: false (but true for CRAN submission checks).

This may give false positives on code which uses
grDevices::densCols and stats:::asSparse as these invoke
KernSmooth and Matrix respectively.

_R_CHECK_CODETOOLS_PROFILE_

A string with comma-separated name=value pairs (with
value a logical constant) giving additional arguments for the
codetools functions used for analyzing package code. E.g.,
use _R_CHECK_CODETOOLS_PROFILE_="suppressLocalUnused=FALSE" to
turn off suppressing warnings about unused local variables. Default: no
additional arguments, corresponding to using skipWith = TRUE,
suppressPartialMatchArgs = FALSE and suppressLocalUnused =
TRUE.

When checking anchored Rd xrefs, use Rd aliases from the CRAN package
web areas in addition to those in the packages installed locally.
Default: false.

_R_SHLIB_BUILD_OBJECTS_SYMBOL_TABLES_

Make the checks of compiled code more accurate by recording the symbol
tables for objects (.o files) at installation in a file
symbols.rds. (Only currently supported on Linux, Solaris, OS X,
Windows and FreeBSD.)
Default: true.

_R_CHECK_CODE_ASSIGN_TO_GLOBALENV_

Should the package code be checked for assignments to the global
environment?
Default: false (but true for CRAN submission checks).

_R_CHECK_CODE_ATTACH_

Should the package code be checked for calls to attach()?
Default: false (but true for CRAN submission checks).

_R_CHECK_CODE_DATA_INTO_GLOBALENV_

Should the package code be checked for calls to data() which load
into the global environment?
Default: false (but true for CRAN submission checks).

_R_CHECK_DOT_FIRSTLIB_

Should the package code be checked for the presence of the obsolete function
.First.lib()?
Default: false (but true for CRAN submission checks).

_R_CHECK_DEPRECATED_DEFUNCT_

Should the package code be checked for the presence of recently deprecated
or defunct functions (including completely removed functions). Also for
platform-specific graphics devices.
Default: false (but true for CRAN submission checks).

_R_CHECK_SCREEN_DEVICE_

If set to ‘warn’, give a warning if examples etc open a screen
device. If set to ‘stop’, give an error.
Default: empty (but ‘stop’ for CRAN submission checks).

_R_CHECK_WINDOWS_DEVICE_

If set to ‘stop’, give an error if a Windows-only device is used in
example etc. This is only useful on Windows: the devices do not exist
elsewhere.
Default: empty (but ‘stop’ for CRAN submission checks on Windows).

_R_CHECK_TOPLEVEL_FILES_

Report on top-level files in the package sources that are not described
in ‘Writing R Extensions’ nor are commonly understood (like
ChangeLog). Variations on standard names (e.g.
COPYRIGHT) are also reported.
Default: false (but true for CRAN submission checks).

_R_CHECK_GCT_N_

Should the --use-gct use gctorture2(n) rather than
gctorture(TRUE)? Use to a positive integer to enable this.
Default: 0.

_R_CHECK_LIMIT_CORES_

If set, check the usage of too many cores in package parallel. If
set to ‘warn’ gives a warning, to ‘false’ or ‘FALSE’ the
check is skipped, and any other non-empty value gives an error when more
than 2 children are spawned.
Default: unset (but ‘TRUE’ for CRAN submission checks).

_R_CHECK_CODE_USAGE_VIA_NAMESPACES_

If set, check code usage (via codetools) directly on the
package namespace without loading and attaching the package and its
suggests and enhances.
Default: true (and true for CRAN submission checks).

_R_CHECK_EXIT_ON_FIRST_ERROR_

If set to a true value, the check will exit on the first error.
Default: false.

_R_CHECK_S3_METHODS_NOT_REGISTERED_

If set to a true value, report (apparent) S3 methods exported but not
registered.
Default: false (but true for CRAN submission checks).

_R_CHECK_OVERWRITE_REGISTERED_S3_METHODS_

If set to a true value, report already registered S3 methods in
base/recommended packages which are overwritten when this package’s
namespace is loaded.
Default: false (but true for CRAN submission checks).

9 R coding standards

R is meant to run on a wide variety of platforms, including Linux and
most variants of Unix as well as Windows and OS X.
Therefore, when extending R by either adding to the R base
distribution or by providing an add-on package, one should not rely on
features specific to only a few supported platforms, if this can be
avoided. In particular, although most R developers use GNU
tools, they should not employ the GNU extensions to standard
tools. Whereas some other software packages explicitly rely on e.g.
GNU make or the GNU C++ compiler, R does not.
Nevertheless, R is a GNU project, and the spirit of the
GNU Coding Standards should be followed if possible.

The following tools can “safely be assumed” for R extensions.

An ISO C99 C compiler. Note that extensions such as POSIX
1003.1 must be tested for, typically using Autoconf unless you are sure
they are supported on all mainstream R platforms (including Windows
and OS X).

A FORTRAN 77 compiler (but not Fortran 9x, although it is nowadays
widely available).

A simple make, considering the features of make in
4.2 BSD systems as a baseline.

GNU or other extensions, including pattern rules using
‘%’, the automatic variable ‘$^’, the ‘+=’ syntax to
append to the value of a variable, the (“safe”) inclusion of makefiles
with no error, conditional execution, and many more, must not be used
(see Chapter “Features” in the GNU Make Manual for
more information). On the other hand, building R in a separate
directory (not containing the sources) should work provided that
make supports the VPATH mechanism.

Windows-specific makefiles can assume GNUmake 3.79
or later, as no other make is viable on that platform.

A Bourne shell and the “traditional” Unix programming tools, including
grep, sed, and awk.

There are POSIX standards for these tools, but these may not
be fully supported. Baseline features could be determined from a book
such as The UNIX Programming Environment by Brian W. Kernighan &
Rob Pike. Note in particular that ‘|’ in a regexp is an extended
regexp, and is not supported by all versions of grep or
sed. The Open Group Base Specifications, Issue 7, which are
technically identical to IEEE Std 1003.1 (POSIX), 2008,
are available at
http://pubs.opengroup.org/onlinepubs/9699919799/mindex.html.

Under Windows, most users will not have these tools installed, and you
should not require their presence for the operation of your package.
However, users who install your package from source will have them, as
they can be assumed to have followed the instructions in “the Windows
toolset” appendix of the “R Installation and Administration” manual
to obtain them. Redirection cannot be assumed to be available via
system as this does not use a standard shell (let alone a
Bourne shell).

In addition, the following tools are needed for certain tasks.

Perl version 5 is only needed for a few uncommonly-used tools: make
install-info needs Perl installed if there is no command
install-info on the system, and for the maintainer-only script
tools/help2man.pl.

Makeinfo version 4.7 or later is needed to build the Info files for the
R manuals written in the GNU Texinfo system.

It is also important that code is written in a way that allows others to
understand it. This is particularly helpful for fixing problems, and
includes using self-descriptive variable names, commenting the code, and
also formatting it properly. The R Core Team recommends to use a
basic indentation of 4 for R and C (and most likely also Perl) code,
and 2 for documentation in Rd format. Emacs (21 or later) users can
implement this indentation style by putting the following in one of
their startup files, and using customization to set the
c-default-style to "bsd" and c-basic-offset to
4.)

10 Testing R code

When you (as R developer) add new functions to the R base (all the
packages distributed with R), be careful to check if make
test-Specific or particularly, cd tests; make no-segfault.Rout
still works (without interactive user intervention, and on a standalone
computer). If the new function, for example, accesses the Internet, or
requires GUI interaction, please add its name to the “stop
list” in tests/no-segfault.Rin.

[To be revised: use make check-devel, check the write barrier
if you change internal structures.]

11 Use of TeX dialects

Various dialects of TeX and used for different purposes in R. The
policy is that manuals be written in ‘texinfo’, and for convenience
the main and Windows FAQs are also. This has the advantage that is is
easy to produce HTML and plain text versions as well as typeset manuals.

LaTeX is not used directly, but rather as an intermediate format for
typeset help documents and for vignettes.

Care needs to be taken about the assumptions made about the R user’s
system: it may not have either ‘texinfo’ or a TeX system
installed. We have attempted to abstract out the cross-platform
differences, and almost all the setting of typeset documents is done by
tools::texi2dvi. This is used for offline printing of help
documents, preparing vignettes and for package manuals via R
CMD Rd2pdf. It is not currently used for the R manuals created in
directory doc/manual.

tools::texi2dvi makes use of a system command texi2dvi
where available. On a Unix-alike this is usually part of
‘texinfo’, whereas on Windows if it exists at all it would be an
executable, part of MiKTeX. If none is available, the R code runs
a sequence of (pdf)latex, bibtex and
makeindex commands.

This process has been rather vulnerable to the versions of the external
software used: particular issues have been texi2dvi and
texinfo.tex updates, mismatches between the two24,
versions of the LaTeX package ‘hyperref’ and quirks in index
production. The licenses used for LaTeX and latterly ‘texinfo’
prohibit us from including ‘known good’ versions in the R
distribution.

On a Unix-alike configure looks for the executables for TeX and
friends and if found records the absolute paths in the system
Renviron file. This used to record ‘false’ if no command
was found, but it nowadays records the name for looking up on the path
at run time. The latter can be important for binary distributions: one
does not want to be tied to, for example, TeX Live 2007.

12.1 Long vectors

Vectors in R 2.x.y were limited to a length of 2^31 - 1 elements
(about 2 billion), as the length is stored in the SEXPREC as a C
int, and that type is used extensively to record lengths and
element numbers, including in packages.

Note that longer vectors are effectively impossible under 32-bit
platforms because of their address limit, so this section applies only
on 64-bit platforms. The internals are unchanged on a 32-bit build of
R.

A single object with 2^31 or more elements will take up at least 8GB of
memory if integer or logical and 16GB if numeric or character, so
routine use of such objects is still some way off.

There is now some support for long vectors. This applies to raw,
logical, integer, numeric and character vectors, and lists and
expression vectors. (Elements of character vectors (CHARSXPs)
remain limited to 2^31 - 1 bytes.) Some considerations:

This has been implemented by recording the length (and true length) as
-1 and recording the actual length as a 64-bit field at the
beginning of the header. Because a fair amount of code in R uses a
signed type for the length, the ‘long length’ is recorded using the
signed C99 type ptrdiff_t, which is typedef-ed to
R_xlen_t.

These can in theory have 63-bit lengths, but note that current 64-bit
OSes do not even theoretically offer 64-bit address spaces and there is
currently a 52-bit limit (which exceeds the theoretical limit of current
OSes and ensures that such lengths can be stored exactly in doubles).

The serialization format has been changed to accommodate longer lengths,
but vectors of lengths up to 2^31-1 are stored in the same way as
before. Longer vectors have their length field set to -1 and
followed by two 32-bit fields giving the upper and lower 32-bits of the
actual length. There is currently a sanity check which limits lengths
to 2^48 on unserialization.

The type R_xlen_t is made available to packages in C header
Rinternals.h: this should be fine in C code since C99 is
required. People do try to use R internals in C++, but C++98
compilers are not required to support these types.

Indexing can be done via the use of doubles. The internal indexing code
used to work with positive integer indices (and negative, logical and
matrix indices were all converted to positive integers): it now works
with either INTSXP or REALSXP indices.

R function length was documented to currently return an
integer, possibly NA. A lot of code has been written that
assumes that, and even code which calls as.integer(length(x))
before passing to .C/.Fortran rarely checks for an
NA result.

There is a new function xlength which works for long vectors and
returns a double value if the length exceeds 2^31-1. At present
length returns NA for long vectors, but it may be safer to
make that an error.

12.2 64-bit types

There is also some desire to be able to store larger integers in R,
although the possibility of storing these as double is often
overlooked (and e.g. file pointers as returned by seek are
already stored as double).

Different routes have been proposed:

Add a new type to R and use that for lengths and indices—most likely
this would be a 64-bit signed type, say longint. R’s usual
implicit coercion rules would ensure that supplying an integer
vector for indexing or length<- would work.

A more radical alternative is to change the existing integer type
to be 64-bit on 64-bit platforms (which was the approach taken by S-PLUS
for DEC/Compaq Alpha systems). Or even on all platforms.

Allow either integer or double values for lengths and
indices, and return double only when necessary.

The third has the advantages of minimal disruption to existing code and
not increasing memory requirements. In the first and third scenarios
both R’s own code and user code would have to be adapted for lengths
that were not of type integer, and in the third code branches for
long vectors would be tested rarely.

Most users of the .C and .Fortran interfaces use
as.integer for lengths and element numbers, but a few omit these
in the knowledge that these were of type integer. It may be
reasonable to assume that these are never intended to be used with long
vectors.

The remaining interfaces will need to cope with the changed
VECTOR_SEXPREC types. It seems likely that in most cases lengths
are accessed by the length and LENGTH
functions25 The current approach is to keep these returning 32-bit lengths and
introduce ‘long’ versions xlength and XLENGTH which return
R_xlen_t values.

12.3 Large matrices

Matrices are stored as vectors and so were also limited to 2^31-1
elements. Now longer vectors are allowed on 64-bit platforms, matrices
with more elements are supported provided that each of the dimensions is
no more than 2^31-1. However, not all applications can be supported.

The main problem is linear algebra done by FORTRAN code compiled
with 32-bit INTEGER. Although not guaranteed, it seems that all
the compilers currently used with R on a 64-bit platform allow
matrices each of whose dimensions is less than 2^31 but with more than
2^31 elements, and index them correctly, and a substantial part of the
support software (such as BLAS and LAPACK) also
work.

There are exceptions: for example some complex LAPACK
auxiliary routines do use a single INTEGER index and hence
overflow silently and segfault or give incorrect results. One example
is svd() on a complex matrix.

Since this is implementation-dependent, it is possible that optimized
BLAS and LAPACK may have further restrictions,
although none have yet been encountered. For matrix algebra on large
matrices one almost certainly wants a machine with a lot of RAM (100s of
gigabytes), many cores and a multi-threaded BLAS.

This is almost unused. The only
current use is for hash tables of environments (VECSXPs), where
length is the size of the table and truelength is the
number of primary slots in use, and for the reference hash tables in
serialization (VECSXPs), where truelength is the number of
slots in use.